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Christopher G. Atkeson is a Professor at the Robotics Institute at Carnegie Mellon University (CMU), where he has been since 2000 after previously holding positions at MIT and Georgia Institute of Technology. His research focuses on fulfilling the science fiction vision of machines achieving human levels of competence in perception, cognition, and action, with particular emphasis on understanding how to get machines to generate and perceive human behavior. Atkeson's work spans two complementary approaches: humanoid robotics and human aware environments. His research interests include nonparametric learning, memory-based learning, reinforcement learning, learning from demonstration, and modeling human behavior. He is particularly known for his work on robot learning of challenging dynamic tasks such as juggling, trajectory-based optimization, and soft robotics (including his contributions to the Baymax character in Disney's Big Hero 6). His recent publications demonstrate a strong focus on tactile sensing (FingerVision), human-in-the-loop optimization for exoskeletons, deep learning for locomotion control, and trajectory-based optimization methods. His work consistently bridges theoretical foundations in machine learning with practical implementations on physical robots. Among his scientific recognitions are an NSF Presidential Young Investigator Award, a Sloan Research Fellowship, and a Teaching Award from the MIT Graduate Student Council. Atkeson has advised numerous students who have gone on to successful careers in academia and industry, including notable researchers like Andrew Moore and Stefan Schaal. His teaching includes courses on dynamic optimization, humanoids, kinematics, dynamics, and control.

• Humanoid robotics
• Human aware environments
• Machine learning
• +13 more

Katie Byl is an Associate Professor in the Departments of Electrical and Computer Engineering and Mechanical Engineering at the University of California, Santa Barbara (UCSB). Her research focuses on robot dynamics and control, particularly in locomotion and manipulation, with applications in rough terrain legged locomotion, supervised autonomy, and flapping-wing flight. She holds B.S., M.S., and Ph.D. degrees in Mechanical Engineering from MIT. Affiliations: Center for Control, Dynamical Systems and Computation (CCDC) Mechanical Engineering Department Research Interests: Byl’s work emphasizes modeling and control of underactuated systems, stochasticity in real-world environments, and the development of robust control principles for dynamic systems. Her applied projects include exoskeletons, autonomous robots like RoboSimian (part of the DARPA Robotics Challenge), and flapping-wing microrobotics. Scientific Awards: NSF Early Career Development Award Hellman Faculty Fellowship Alfred P. Sloan Foundation Fellowship in Neuroscience Regents’ Junior Faculty Fellowship Teaching & Advising: Byl teaches courses in control systems (e.g., ECE 147B, ECE 179D) and robotics. She advises students in UCSB’s Robotics Lab, emphasizing a mix of control theory, mechanical design, and algorithm development. Undergraduate researchers are also recruited annually for summer projects. Labs & Teams: Her Robotics Lab focuses on hardware implementation of control ideas, maintaining a high robot-to-student ratio. Collaborations include the Army’s Institute for Collaborative Biotechnologies and the DARPA Robotics Challenge with JPL.

• Robotics
• Control Systems
• Robot Dynamics and Control
• +7 more

Wei Zhang is a tenured Professor at the Southern University of Science and Technology (SUSTech) , Shenzhen, China, and a Senior Member of IEEE. He serves as Associate Editor for IEEE Transactions on Control System Technology and leads the CLEAR Lab (Control & Learning for Robotics and Autonomy) within the School of Automation and Intelligent Manufacturing (AiM). His career spans institutions including the University of California, Berkeley (postdoc), and The Ohio State University (Assistant/Associate Professor). Education: PhD in Electrical Engineering from Purdue University (2009), MS in Electrical and Computer Engineering from University of Kentucky (2005), BS in Automation from University of Science and Technology of China (2003) Research Interests focus on integrating control theory, optimization, and machine learning to develop intelligent systems. Key areas include: Legged Robots: Dynamic locomotion control, bio-inspired gait design, and push recovery mechanisms Autonomous Systems: Real-time motion planning, obstacle avoidance, and safe navigation in adversarial environments Smart Grids: Distributed control for energy systems and transactive energy optimization Machine Learning: Reinforcement learning for robotics, Q-learning convergence analysis, and hybrid control-learning frameworks Publication Trends highlight interdisciplinary work at the intersection of robotics and control systems. His recent 2024 papers address: Whole-body control for wheeled-quadrupedal robots Geometric object pose refinement in computer vision Task-space Riccati feedback for underactuated systems Teacher-student reinforcement learning architectures Scientific Awards include: 2016 : NSF CAREER Award 2015 : Lumley Research Award (Ohio State University) 2013 : AFOSR Summer Faculty Fellowship 2018 : National Distinguished Expert (Young, China) 2019 : International Underwater Robot Competition 2nd Prize (team advisor) Academic Leadership involves editorial roles at IEEE Transactions on Control System Technology and IEEE Transactions on Power Systems. His lab provides state-of-the-art robotics platforms including quadruped robots, Kuka manipulators, and UAVs for algorithm validation. Research collaborators span The Ohio State University , UC Berkeley , CMU , and The University of Hong Kong .

• Control Theory
• Optimization Theory
• Machine Learning
• +5 more

Aaron D. Ames is the Bren Professor of Mechanical and Civil Engineering, Control and Dynamical Systems, and Aerospace at the California Institute of Technology (Caltech). He serves as the Booth-Kresa Leadership Chair and Director of the Center for Autonomous Systems and Technologies since 2025. His academic journey includes a BS in Mechanical Engineering and BA in Mathematics from the University of St. Thomas (2001), followed by an MA in Mathematics and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2006). Research Interests span theoretical and experimental work in hybrid systems, nonlinear control, and bipedal robotic locomotion. Specific focus areas include: Control Barrier Functions (CBFs) for safety-critical systems Powered prostheses and robotic assistive devices Legged locomotion for bipedal and quadrupedal robots Cyber-physical systems and autonomous control Model predictive control and real-time optimization Humanoid robotics and geometric safety filters Recent publications emphasize risk-aware control, safety verification, and locomotion on constrained environments, with applications to drones, exoskeletons, and quadrupedal robots. His work often integrates theoretical advancements with practical validation through the AMBER Lab. Scientific Awards & Honors : NSF CAREER Award (2010) Donald P. Eckman Award (2015) Leon O. Chua Award (2005) Bernard Friedman Prize (2006) Teaching includes graduate courses on nonlinear control, hybrid systems, and robotics at Caltech, alongside prior roles at Georgia Tech and Texas A&M. He leads the AMBER Lab (Bipedal Robotics) and has secured major grants from the National Science Foundation, NASA, and industry partners like Miso Robotics and SRI International.

• Bipedal Robotics
• Hybrid Systems
• Nonlinear Control
• +7 more

Atsuo Takanishi is a distinguished Professor at Waseda University's Faculty of Science and Engineering, School of Creative Science and Engineering, with appointments in both the Department of Bioscience and Biotechnology and the Department of Modern Mechanical Engineering. With over 700 publications, 10,399 citations, and an h-index of 47, Professor Takanishi has established himself as a leading figure in robotics research. His work spans multiple institutions, including visiting positions at Showa University School of Dentistry and KDDI Research, Inc. Professor Takanishi received his Doctor of Engineering degree from Waseda University in 1985, following his undergraduate studies in the Faculty of Science and Engineering at the same institution, which he completed in 1980. His academic journey began with positions as an Assistant at Waseda University (1985-1988), Lecturer (1988-1990), and Associate Professor (1990-1997) before achieving his current Professor status. Professor Takanishi's research interests center around robotics and intelligent systems, with particular expertise in humanoid robotics, surgical dentistry applications, and biomechatronics. His work demonstrates a unique integration of mechanical engineering principles with biological systems, resulting in innovative robotic platforms that mimic human capabilities. Notably, his laboratory has developed the Waseda Anthropomorphic Saxophonist Robot series and various medical training simulators that have significantly advanced the field of human-robot interaction. His research in experimental psychology complements his robotics work, particularly in human-robot interaction studies. Analysis of Professor Takanishi's recent publications reveals a strong focus on practical applications of robotics technology, particularly in assistive devices for elderly mobility, medical training simulators, and agricultural robotics for Synecoculture environments. His work consistently bridges theoretical robotics with real-world implementation, with recent papers emphasizing reinforcement learning approaches, haptic feedback systems, and computer vision techniques for complex environments. The Waseda Anthropomorphic Saxophonist Robot series represents a culmination of decades of research in humanoid robotics and musical expression. IEEE Fellow (2020-present) Robotics Society of Japan Fellow (2012-present) Japan Society of Mechanical Engineers Fellow (2007-present) Multiple Best Paper Awards from IEEE conferences (ICRA, IROS, ROMANSY) BusinessWeek Asia's Stars Bloomberg (2001) IFToMM Award of Merit (2010) Professor Takanishi has maintained extensive collaborations with research institutions worldwide, particularly with Italian institutions through the Italy-Japan Workshop series. His laboratory has secured substantial funding from Japanese government agencies including the Japan Science and Technology Agency, with particular emphasis on projects bridging robotics with medical applications and human-centered technologies. His international recognition is evidenced by his numerous visiting professorships and collaborative projects across continents. Professor Takanishi leads the Takanishi Laboratory at Waseda University, which maintains specialized facilities for humanoid robotics development, medical robotics research, and human-robot interaction studies. The laboratory has developed several notable robotic platforms including the WABIAN series of bipedal humanoid robots, the WASEDA FLUTIST ROBOT, and various medical training simulators used in clinical education settings. His work on the WASEDA SAXOPHONIST ROBOT represents a unique fusion of musical artistry and robotics engineering.

• Robotics
• Mechatronics
• Humanoid Robots
• +4 more

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +4 more

Georg Martius is a Full Professor in the Department of Computer Science at the University of Tübingen's Faculty of Science and a Max Planck Research Group Leader at the MPI for Intelligent Systems. Since April 2023, he has been a core member of the DFG-funded Cluster of Excellence 'Machine Learning: New Perspectives for Science,' which received extended funding through 2032 for its mission to integrate machine learning into fundamental scientific discovery processes. His academic foundation includes a PhD from the University of Göttingen and Bernstein Center for Computational Neuroscience (2005), a Diploma in Computer Science from the University of Leipzig (2003), and a visiting research period at the University of Edinburgh's Division of Informatics. Postdoctoral positions followed at the Max Planck Institutes for Dynamics and Self-Organization (Göttingen, 2009), Mathematics in the Sciences (Leipzig, 2010), and IST Austria (2015). Professor Martius's research pioneers the intersection of reinforcement learning, robotics, and tactile sensing, with emphasis on developing autonomous systems capable of natural locomotion, dexterous manipulation, and physical-world understanding. His work bridges theoretical machine learning with practical hardware applications, particularly in creating differentiable simulators, superresolution tactile sensors, and biologically plausible learning frameworks for robotic control. Analysis of his 2024-2025 publications reveals dominant trends in offline reinforcement learning (especially goal-conditioned and diversity-maximization techniques), object-centric representation learning for video understanding, and tactile sensing innovations. A strong thread connects foundation models to world model construction, while his work on differentiable physics engines enables precise collision handling and contact dynamics for real-world robotic control. His leadership roles include directing the Distributed Intelligence research team at Tübingen and contributing to major collaborative initiatives like the Real Robot Challenge and Myochallenge 2022. The Cluster of Excellence appointment represents recognition of his contributions to transforming scientific methodology through machine learning, particularly in automating hypothesis generation and experimental design. Current projects focus on integrating large-scale machine learning with embodied intelligence, advancing tactile perception systems like the Minsight vision-based sensor, and developing neuroplasticity-inspired approaches for robust out-of-distribution detection. His work directly impacts fields requiring physical interaction intelligence, from autonomous navigation to medical robotics, with emphasis on sample-efficient learning from limited real-world data.

• Machine Learning
• Reinforcement Learning
• Robotics
• +3 more

Professor Sangbae Kim is the Jerry McAfee (1940) Professor in Engineering at the Massachusetts Institute of Technology (MIT), School of Engineering, Department of Mechanical Engineering. His research focuses on bio-inspired robotics, extracting principles from animal biomechanics to develop high-performance robotic systems. Education: B.S. from Yonsei University (2001), M.S. (2004) and Ph.D. (2008) from Stanford University. Research Interests: Bio-inspired Robotics, Robotic Actuators, Locomotion Dynamics, Composite Sensor Fabrication, and Minimally Invasive Surgical Robotics. His notable achievements include the MIT Cheetah robot capable of 13mph outdoor running and autonomous obstacle jumping, and Stickybot, a climbing robot featured in TIME's Best Inventions (2006). Recent publications emphasize soft robotics, energy-efficient legged locomotion, and bio-inspired actuator design. Kim has received prestigious awards including the NSF CAREER Award (2014), DARPA Young Faculty Award (2013), and Ruth and Joel Spira Award for Distinguished Teaching (2015). Scientific Awards: NSF CAREER (2014), DARPA YFA (2013), TIME Best Invention (2006), multiple best paper awards. Professional Service: Associate Editor roles, NSF review panels, and leadership in IEEE and ASME organizations.

• Bio-inspired Robotics
• Bipedal Locomotion
• Manipulation
• +7 more

Justin Yim is an Assistant Professor at the Department of Mechanical Science and Engineering at the University of Illinois Urbana-Champaign (UIUC), where he runs the Novel Mobile Robots Lab (NMbL). His research focuses on enabling high-performance locomotion in robots through concurrent design of mechanisms and controllers, inspired by biological systems. He previously earned his PhD in Electrical Engineering from UC Berkeley (2020) and dual BS degrees in Mechanical Engineering and Applied Mechanics/Electrical Engineering from the University of Pennsylvania (2015), followed by a postdoctoral researcher role at Carnegie Mellon University (2020-2022). PhD, Electrical Engineering, University of California, Berkeley (2020) MSE, Robotics, University of Pennsylvania (2015) BSE, Mechanical Engineering and Applied Mechanics/Electrical Engineering, University of Pennsylvania (2015) His research explores legged robot design, bioinspired robotics, and locomotion dynamics, with a focus on overcoming terrain challenges through minimalist mechanical systems and control strategies. Recent work emphasizes squirrel-inspired jumping and landing mechanics, programmable substrates for locomotion studies, and energy-efficient robot mobility. Selected article trends highlight innovations in monopedal hopping with series-elastic actuators, bioinspired balance control, underactuated bipedal walkers, and cooperative cable-driven modular robots. His work bridges theoretical insights with practical applications in extreme-terrain mobility. NSF CAREER Award (2025): 'Extreme Robot Walking: Speed, Agility, and Efficiency via Reduced Degrees of Freedom' NASA Innovative Advanced Concepts Fellow (2025) Justin Yim actively mentors graduate students and leads research projects in the NMbL lab, which develops robots capable of walking, hopping, and rolling in complex environments. Recent lab achievements include a Best Demo award at the 2nd Unconventional Robots Workshop (2025) and awards for outstanding locomotion papers. He teaches courses such as ME 370 Mechanical Design I and SE 422 (ME 446, ECE 489) Robot Dynamics and Control.

• Legged Robotics
• Bioinspired Robotics
• Robot Locomotion and Dynamics
• +3 more

Yan Gu is an Associate Professor of Mechanical Engineering at Purdue University, located in West Lafayette, Indiana. He is affiliated with the School of Mechanical Engineering within the College of Engineering. His research focuses on legged locomotion, humanoid and quadrupedal robots, wearable robotics, hybrid dynamical systems, control systems, state estimation, and dynamics. He leads the TRACE Lab and has been recognized with prestigious awards including the NSF CAREER Award (2021) and multiple teaching accolades. Gu holds a Ph.D. from Purdue University (2017) and a B.S. from Zhejiang University, China (2011). His work emphasizes robust control strategies for legged robots in dynamic environments, including adaptive ankle torque control, time-varying foot-placement algorithms, and state estimation techniques for non-inertial surfaces. His recent publications explore multimodal datasets in animal-robot interaction and the stabilization of quadrupedal locomotion on accelerating platforms. His research has been supported through grants such as the NSF CAREER Award, and his contributions span both theoretical advancements in hybrid control systems and practical applications in wearable robotics and exoskeleton design. Gu’s TRACE Lab serves as a hub for innovative robotics research, addressing challenges in robot-environment interaction and dynamic stability.

• Legged locomotion
• Humanoid and quadrupedal robots
• Wearable robotics
• +5 more

Dr. Philipp Allgeuer is a Postdoctoral Research Associate at the Knowledge Technology Research Group within the Department of Informatics at the University of Hamburg. His work focuses on humanoid robotics, bipedal locomotion, and sensor fusion. He holds a PhD from the Autonomous Intelligent Systems Group at the University of Bonn, alongside dual bachelor's degrees in Mechatronic Engineering and Mathematical/Computer Sciences (both with First-Class Honors). His research contributions include the development of the igus Humanoid Open Platform and the NimbRo-OP series of humanoid robots, recognized with awards like the RoboCup HARTING Open Source Award (2016) and the Best Humanoid Award (2018). He has authored influential papers on fused angles for robot balance, tilt phase space representations, and neuro-inspired control architectures. Allgeuer's teams have dominated RoboCup competitions, winning titles in AdultSize and TeenSize leagues multiple times. His open-source software frameworks (e.g., rot_conv_lib , attitude_estimator ) and hardware designs are widely used in robotics research. Recent work explores multimodal human-robot interaction and AI-driven robotic task coordination. He is affiliated with the Knowledge Technology Research Group and contributes to projects like the NICOL humanoid robot, bridging social interaction and reliable manipulation. His research spans from low-level control algorithms to high-level behavior planning systems.

• Robotics
• Humanoid Robotics
• Bipedal Locomotion
• +5 more

Robert D. Gregg, IV is a Professor of Mechanical Engineering, Robotics, and Electrical & Computer Engineering at the University of Michigan. He serves as Associate Director for Graduate Education at Michigan Robotics and directs the Locomotor Control Systems Laboratory. His research focuses on control systems for wearable robots, prosthetics, and orthotics, emphasizing biomimetic principles and nonlinear control theory. Gregg holds a PhD from the University of Illinois at Urbana-Champaign (2010) and prior academic roles at the University of Texas at Dallas and Northwestern University. Education: PhD, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2010 MS, Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2007 BS, Electrical Engineering and Computer Sciences, University of California, Berkeley, 2006 Research Interests: Gregg’s work spans control mechanisms for bipedal locomotion, wearable robotics, nonlinear control theory, and rehabilitation engineering. His lab develops high-performance control systems for prosthetic legs and exoskeletons to enhance mobility for individuals with disabilities. Key areas include energy-efficient control strategies, adaptive impedance systems, and biomechanical modeling of human movement. Grant & Award Highlights: $3M NIH R01 Grant (2023): Modeling and control of agile powered prosthetic legs for varied activities. $1.7M NIH R01 Grant (2021): Modular powered orthoses for broad patient populations. NSF CAREER Award (2017) NIH New Innovator Award (2013) Advising & Labs: Gregg mentors PhD students in robotics and biomechanics, emphasizing independent research and collaborative team environments. His lab supports over 15 researchers and has produced notable alumni like Dr. Cara Welker (University of Colorado Boulder faculty). The lab’s work is supported by NIH, NSF, and industry partnerships. Recent Contributions: Recent work includes phase-variable control for stair climbing, energy shaping methods for exoskeletons, and open-source robotic leg platforms. Gregg also chairs major robotics conferences (e.g., IROS 2023) and advises on clinical translation of wearable robotics.

• Wearable Robots
• Legged Robots
• Prosthetics & Orthotics
• +3 more

Petar Kormushev is a Senior Lecturer (Associate Professor) in Robotics at the Dyson School of Design Engineering, Imperial College London, and the founder/director of the Robot Intelligence Lab. He holds a PhD in Computational Intelligence from Tokyo Institute of Technology. His research focuses on robotics and machine learning, particularly reinforcement learning for autonomous robots. Key projects include the WALK-MAN humanoid robot for disaster response and contributions to the PANDORA and STIFF-FLOP EU projects. He has received the 2013 John Atanasoff Award for scientific excellence in ICT. His lab develops machine learning algorithms applied to humanoid robots like COMAN and iCub, with interests in robot learning, compliant control, and autonomous systems. He has supervised numerous PhD students and led research teams at IIT and Imperial College.

• Robotics
• Machine Learning
• Reinforcement Learning
• +4 more

Alireza Ramezani is an Associate Professor of Electrical and Computer Engineering at Northeastern University, leading the SiliconSynapse Lab. He focuses on bio-inspired robotics, nonlinear systems, and robot locomotion, with a particular emphasis on morphological design and control inspired by biological systems. His work integrates control theory and experimental robotics to develop robots capable of navigating confined spaces, such as caves and ducts, using mechanisms derived from bat movements. Education : PhD, Mechanical Engineering, University of Michigan (2014) MS, Mechanical Engineering, ETH Zurich (2010) BSc, Mechanical Engineering, Iran University of Science and Technology (2007) Research Interests : Design of robots with non-traditional morphologies Nonlinear feedback control systems Legged and fluidic-based locomotion Bio-inspired robotics and biology-driven engineering Awards : 2024 ASME Rising Star Award 2024 NSF CAREER Award 2022 NASA Game Changing Program Award Science Magazine Top 5% Research Output (2020) Advising & Labs : Ramezani mentors students in projects like the NASA-funded “Crater Observing Bio-inspired Rolling Articulator” and oversees the SiliconSynapse Lab, which develops robots for space exploration and confined environments. Notable advisees include Henry Noyes, a NASA Space Technology Fellow. Labs/Teams : His lab collaborates with institutions like NASA’s Jet Propulsion Lab (JPL) on projects such as the Mars Multi-modal Morphing (M4) Rover and bio-inspired snake robots for lunar crater exploration.

• Robotics
• Control Theory
• Nonlinear Systems
• +3 more

Luis Sentis is a Professor in the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin and holds the Frank and Kay Reese Endowed Professorship in Engineering . He leads the Human Centered Robotics Laboratory, focusing on control systems, human-robot interaction, and exoskeleton robotics. His affiliations include UT Austin's Good Systems initiative and Apptronik Systems as an innovation advisor. Ph.D. , Electrical Engineering, Stanford University B.S. , Telecommunications and Electronics Engineering, Polytechnic University of Catalonia His research spans humanoid robotics , agile manipulation , autonomous systems , and human-robot teaming . Recent work emphasizes FAIR datasets , EEG monitoring , and collision detection for legged robots, with applications in industrial automation and ethical AI. Scientific awards include the NASA Elite Team Award and La Caixa Foundation Fellowship . Funding sources include DARPA , NSF , NASA , and ONR .

• Robotics
• Humanoid Robotics
• Autonomous Systems
• +7 more