Legged and wheeled robot locomotion.
Octi Zhang, Mateo Guaman Castro, Patrick Yin et al.
General-purpose robots must perform a wide range of tasks from agile locomotion to dexterous manipulation.
Yufei Jia, Zhanxiang Cao, Mingrui Yu et al.
Simulation-based RL for contemporary robot control is increasingly organized around GPU-resident simulation: physics, rollout collection, and learning are placed on a single GPU-centric execution path
Hanjun Kim, Sixu Zhou, John Shim et al.
Existing biomimetic lower-limb prosthesis controllers are commonly designed for discrete locomotion modes, requiring separate parameterizations for level walking, ramps, and stairs.
Jinwoo Hwang, Ilseung Park, Changseob Song et al.
Objective: Data-driven models that estimate physiological states, particularly biological joint moments, are widely used in exoskeleton control.
Junheng Li, Liang Wu, Sergio A. Esteban et al.
In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (RL) enables robust whole-body skills through large-scale simulation. However, using MPC inside RL often requires time-consuming problem construction or excessive training overhead, making such frameworks difficult to justify in practice. This work studies efficient training-time MPC guidance for humanoid locomotion and manipulation, termed MPC-RL. We introduce a centroidal-dynamics MPC reward formulation that leverages guidance from MPC trajectories in training time. To make this practical in massively parallel RL, we develop π^nMPC, a parallel-in-horizon and construction-free batched GPU MPC solver that operates directly on time-varying dynamics to avoid high memory usage and pre-compilation. Through a variety of comparative studies and hardware validations, we have found that MPC-RL achieves superior performance in locomotion and manipulation skills.
Iana Zhura, Didar Seyidov, Dmitrii Plotnikov et al.
Navigating toward human callers is an important capability for rescue robots operating where visual contact is degraded or occluded. We present AcousticDiffusion, a semantically conditioned, audio-guided diffusion policy for human-directed navigation.
Satoshi Yamamori, Koji Ishihara, Kentaro Minamikawa et al.
Instead of making simulation imitate every motor imperfection, this work shapes the physical actuator to follow the simulated reference.
I-Chia Chang, Jacob Stewart, Petros A. Ioannou et al.
Legged robot control is not yet practical in non-inertial environments, where the ground and surroundings accelerate in the inertial Earth frame, mainly because of the unknown, time-varying, persistent perturbations caused by environmental motion, compounded by the associated hybrid robotic dynamics.
Teeratham Vitchutripop, Alyssa Quarles, Wenhe Zhang et al.
Over the course of a lifetime, robots may encounter novel scenarios unaccounted for in its original training that result in performance degradation. One common approach to mitigating this issue is to further grow the offline training dataset in hopes of producing a policy robust to these changes.
Jan C. L. Lau, Christian Mele, Jonathan Feng-Shun Lin et al.
Human gait in reality extends beyond straight-line walking, with some situations even requiring drastic back-and-forth rotations. A smooth and stable execution may seem intuitive, but the underlying mechanics remains unknown.
Kaifeng Zhao, Mathis Petrovich, Haotian Zhang et al.
Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics.
Hongjin Chen, Zijun Xu, Shihao Ma et al.
Humanoid locomotion across complex terrain demands forward-looking exteroception to anticipate obstacles, yet this signal is unreliable in real-world deployment, failing partially and intermittently.
Al Jaber Mahmud, Shuai Li, Xuan Wang
In this paper, we study the joint selection of an environmental support contact and a whole-body configuration for a prescribed loco-manipulation task. A contact may provide greater physical support while restricting the motion required for the task.
Jiayun Li, Georgia Chalvatzaki
Contact-rich motion planning (CRMP) is essential for robotic manipulation and locomotion, yet remains computationally challenging due to combinatorial contact decisions.
Siyuan Ma, Boshi Zhang, Yutian Zhang et al.
Mobile manipulation requires a robot to predict how locomotion and arm motion jointly alter future observations and control. Existing world-action models, developed largely for fixed-base platforms, do not explicitly distinguish camera ego-motion from base and arm actions.
Liang Zhou, Jiaming Su, Yancong Wei et al.
Quadruped manipulators enable mobile grasping in complex environments, yet their whole-body control policies remain vulnerable to unreliable onboard visual perception.
Jaskrit Singh, Kashif K. Noori, Jing Xiao et al.
Off-road traversability is direction-dependent and vehicle specific, yet most global maps assign a single isotropic cost to each location.
Pranav Wagh, Yu Fang, Yue Yang et al.
Long-horizon robotic manipulation is often built by chaining independently trained skills.
David Nguyen, Marcelo Coelho, Sangbae Kim
Robot performance is often limited by the cost of iterating on morphology and control together, since every computer-aided design (CAD) change has to be carried into a simulation-ready model before control work begins.
Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin et al.
Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons. Dual goal representations provide value fields that capture global goal reachability, but they do not directly specify which action should be preferred at a given state.
Kapalik Khanal, Biswash Khatiwada, Stephen Afrifa et al.
Changing a humanoid robot's manipulation policy should not require retraining the controller that maintains the robot's balance.
Jimin An, Ryan Lee, Jingshu Peng et al.
Biological torque control directly maps an estimated human joint moment to exoskeleton assistance, providing a task-agnostic strategy for supporting diverse locomotor activities.
Songyuan Zhang, Baljeet Singh, Sarthak Ranjeet Kaingade et al.
Safe reinforcement learning commonly places safety and task performance in the same policy objective, where they can introduce competing updates.
Pavel Bushuyeu, Yujin Chen, Anton Nikolaev et al.
Existing benchmarks evaluate tabletop manipulation, flat-floor household activity, or humanoid locomotion and manipulation as separate task groups; none scores vertical mobility and dexterous work on a fragile payload in one long-horizon episode.
Mohammadmehdi Ataei, Mohammad Amin Nabian, Jie Xu et al.
Compared with analytical physics, learned dynamics models promise robot simulation that is faster, inherently differentiable, and easily adaptable to real data.
Lav R. Varshney
Open-loop multilegged locomotion over rough terrain has been interpreted as matter transport over a noisy channel: leg-ground interactions are discrete basic active contacts, terrain deletes or perturbs those contacts, and spatial redundancy concentrates the resulting thrust and arrival time.
Haoxuan Han, Chen Chen, Linao Gong et al.
Humanoid robots have achieved strong locomotion capabilities, but reliable navigation on versatile terrains remains challenging because obstacle avoidance must be coordinated with dynamically feasible motion.
Javier Borquez, Shuang Peng, Somil Bansal
Hybrid dynamical systems provide a powerful modeling framework for robotic systems, particularly in contact-rich environments. However, ensuring safety and performance in such systems remains challenging due to the intricate coupling between continuous dynamics and discrete mode transitions.
SenseNova, Joint Research Team, : et al.
GPT-6-Astra demonstrates a level of competence in robotic manipulation and navigation that challenges the conventional paradigm of building specialized models for individual tasks and embodiments.
Derek You, Zafir Shamsi, Keqin Wang et al.
Actuator degradation turns quadruped locomotion into a coordination problem requiring joints to compensate for lost actuation. Prior work suggests that morphology-aware graph policies improve learning and generalization under body perturbations.
Yangzhi Yang, Xiansheng Lin, Zhaoming Xie et al.
Humanoid robots could transport payloads substantially heavier than themselves by pulling passive wheeled vehicles instead of carrying the load.
Kyochul Jang, Seohyeon Park, Ohchul Kwon et al.
As robotic hardware and learning methods advance, humanoids need tools to perform tasks beyond their inherent physical limits. Successful tool use requires selecting a suitable tool and coordinating manipulation and, when needed, locomotion to complete the task.
Sangwoo Shin, Ashvin Anilkumar, Ryan Gao et al.
Sampling-based system identification estimates physically meaningful parameters by tuning a simulator to reproduce the target system dynamics, providing an interpretable approach to improving sim-to-real transfer.
\"U. Bora G\"okbakan (WILLOW), St\'ephane Caron (ISIR), Philippe Sou\`eres (LAAS-GEPETTO)
Active perception allows autonomous agents to select their viewpoints rather than passively process the viewpoints given to them, enabling them to target where to reduce uncertainty about their environment.
William D. Compton, Zachary Olkin, Ryan Bena et al.
Research in humanoid robotics has achieved rapid progress in locomotion, and recent results have pushed the boundary on autonomous navigation.
Junnosuke Kamohara, Feiyang Wu, Andy Ningan Zong et al.
Humanoid locomotion on granular terrain remains a significant challenge due to its complex foot-terrain interaction dynamics that are difficult to model. Existing approaches either ignore granular contact dynamics or incorporate simplified normal force models with heuristic tangential components.
Qinglian Wu, Yidian Hu, Weiqi Fan et al.
Long-range throwing on unstructured terrain is a key capability for mobile robots in delivery and retrieval tasks, but it requires high release velocity, coordinated whole-body motion, and robustness to terrain-induced disturbances.
Zuxing Lu, Ziang Zheng, Yao Lyu et al.
Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstream tasks, including locomotion and loco-manipulation.
Zewen He, Taiki Ishigaki, Ko Yamamoto
Physical flexibility plays a significant role not only in human motions but also in robot motions.
Vittorio Palladino, Teaya Yang, Ruiqi Zhang et al.
Autonomous UAV flight through cluttered and partially unknown environments requires reasoning not only about observed obstacles but also about occluded regions that the sensor cannot observe.
Xiangrui Wang, Yuetong Wu, Jalen Beeman et al.
Open datasets describing human behavior in environments shared with mobile robots remain limited, particularly for retail activities that combine locomotion, reaching, object handling, and robot guided movement.
Pranay Dugar, Mohitvishnu S. Gadde, Rajesh Mangannavar et al.
Recent progress in learned whole-body controllers for humanoid robots has enabled robust free-space motion by tracking kinematic targets such as joint or end-effector trajectories.
Friedrich Graaf, Elias Birkefeld, Christian Eichmann et al.
Legged robots offer a variety of automation applications in real-world scenarios. But areas that are difficult to traverse, like slopes, caves, or scaffolding, still pose a great challenge for traversal.
Hyeonjin Choi, Joongheon Kim, Daekyum Kim
Learning energy-efficient humanoid locomotion requires discovering mechanically economical gait coordination, not merely reducing actuator effort. Reinforcement learning promotes efficiency through effort-related reward penalties, which guide the step-to-step mechanics of walking only indirectly.
Ruiqi Wang, Dezhong Zhao, Xuesu Xiao et al.
PrefO2O aims to remove the per-task cost of preference-based RL by reusing human preferences from related source tasks, so a new target task needs no human preference labels.
Natapat Kirdwichai, Tobias Driskell-Poole, Andrei Sontea et al.
Depth-conditioned locomotion policies have demonstrated impressive agile maneuvers, but can be steered to unpredictable actions when observations are outside their training distribution.
Jian Zhou, Xingyu Zhang, Rui Ma et al.
Muscle-driven locomotion provides a physically grounded approach to generating realistic human movement. However, achieving both physiological plausibility and adaptability to changes in musculoskeletal capacity and external disturbances remains a fundamental challenge.
Shutong Ding, Zejia Zhong, Zhongyi Wang et al.
Recent advances in reinforcement learning (RL) have achieved great successes by leveraging the multimodality and exploration capability of diffusion policies. Among these approaches, one representativ
Myeongju Cha, Pilwon Hur
This paper presents sparse calibration-based personalization for kernel-based gait phase and walking speed co-estimation from wearable inertial measurement units (IMUs). A 28-speed reference library was constructed from bilateral thigh and shank trajectories in a 20-subject locomotion dataset.
Xiao Ren, Yuhui Yang, Zongbiao Weng et al.
Recent reinforcement learning approaches have shown great promise in improving humanoid motion tracking performance and achieving fall recovery under disturbances.
Liao, Qiayuan, Shao, Yiyang, Long, Junfeng et al.
Tracking large human-motion datasets has become the dominant route to general, expressive humanoid whole-body control, but scaling across many motions is expensive.
Yujie Xiong, Peng Zhai, Taixian Hou et al.
Brachiation enables primates to move across overhead supports when ground paths are blocked, suggesting a complementary locomotion mode for robots operating in cluttered or hazardous environments.
Yuto Tanaka, Kyo Kutsuzawa, Martina Doku et al.
Imitation learning enables robots to acquire complex skills directly from massive demonstration datasets, but its performance degrades severely when datasets are contaminated with suboptimal or noisy demonstrations.
Sungsan Park, Young-Sik Shin, Sanghyun Kim
Quadruped robots entering hospitals, care homes, and quiet offices must be context-appropriate not only in where they move but in how loudly they move: a legged robot's locomotion noise, dominated by foot-ground impacts, is itself a social variable.
Joochan Kim, Chanuk Yang, Tackgeun You et al.
We present Go2-DrivoR, a goal-conditioned adaptation of the end-to-end autonomous driving trajectory planning framework DrivoR for urban navigation with quadrupedal robots.
Huaze Liu, Zhenyu Wu, Jaehwi Jang et al.
Navigation in vision-denied environments is challenging for humanoid robots because proprioceptive odometry drifts and localization uncertainty accumulates rapidly.
Lemon Foxmere, Anthony Furman, Yizheng Du et al.
Reinforcement Learning (RL) has enabled legged robots to perform a range of skills in single-task settings. However, applications such as farm robotics or space exploration require diverse skills such as locomotion, digging, or close-range surveying.
Mahmud Hasan Saikot, Sydney Spiegel, Sudheera Akalanka Kariyawasam et al.
Robots that can change their morphologies and behaviors for different tasks and environments hold great promise for adaptable, multifunctional systems.
Long Zhao, Xingjue Liao, Feifei Qian
Locomotion on granular slopes such as sand dunes remains a fundamental challenge for legged robots due to reduced shear strength and gravity-induced anisotropic yielding of granular media.
Cong-Thanh Vu, Yen-Chen Liu
In the field of Human-Robot Interaction (HRI), achieving flexibility in human-accompanying within real-world environments holds great potential for various applications but also poses significant challenges.
Xincheng Tang, Yiji Chen, Youhan Xie et al.
Door opening and traversal is a long-horizon loco-manipulation task that requires precise handle interaction and coordinated base-arm control. We present Video2DoorTraversal, a single-video real-to-sim-to-real framework for wheel-legged mobile manipulators.
Mert Albaba, Jens Beißwenger, Anna Manasyan et al.
Teaching a humanoid to follow instructions with its whole body runs into two obstacles. Its action space is large and tightly coupled: legs, arms, and fingers must move together while the robot keeps its balance, which makes joint-level actions hard to learn.
Zhe Li, Zhenzhe Zhang, Yangyang Wei et al.
Humanoid household tasks often require concurrent loco-manipulation, where the robot must move, adjust posture, maintain balance, and manipulate objects as a single coordinated behavior.
Haoran Jiang, Jin Chen, Qingwen Bu et al.
Humanoid robots require precise locomotion and dexterous manipulation to perform challenging loco-manipulation tasks. Yet existing approaches, modular or end-to-end, are deficient in manipulation-aware locomotion.
Nikita Rudin, Fabian Jenelten, Timon Homberger et al.
Unitree Go2 uses cross-modal vision + proprioception transformers trained in Isaac Gym to achieve agile navigation over rough terrain with minimal sim-to-real gap.
Nikita Rudin, David Hoeller, Philipp Reist et al.
Legged Gym is an open-source training framework built on NVIDIA Isaac Gym that enables massively parallel deep RL for legged robot locomotion, achieving policy training times of minutes rather than days.
Nikita Rudin, David Hoeller, Marco Hutter
RSL-RL is a lightweight, high-performance PyTorch library implementing PPO tailored for locomotion. Used as the default RL backend in Legged Gym, it enables training thousands of agents in parallel on GPU.
Read these papers in order to build expertise in Locomotion.
Octi Zhang, Mateo Guaman Castro et al. · 2026
Yufei Jia, Zhanxiang Cao et al. · 2026
Hanjun Kim, Sixu Zhou et al. · 2026
Jinwoo Hwang, Ilseung Park et al. · 2026
Junheng Li, Liang Wu et al. · 2026
Iana Zhura, Didar Seyidov et al. · 2026
…and 61 more papers.
