Robotic Locomotion and Control

Últimos artículos

• TERRA: Terrain-Aware Reconstruction, Retargeting and Control for Musculoskeletal Locomotion

  Merkourios Simos, Chengkun Li, Bianca Ziliotto, Alexander Mathis · 1 de octubre de 2026

  Recent advances in musculoskeletal modeling and reinforcement learning have enabled muscle-actuated agents to reproduce increasingly complex human motions. Yet these capabilities remain largely confined to flat ground, in part because motion datasets rarely include aligned terrain geometry and becau…

• Predictive Safety Curricula for Robust Legged Locomotion

  Ivan Ovinnikov, Pascal Sutter, Christian Gehring, Jordis Herrmann · 30 de septiembre de 2026

  Rare but consequential failures can persist in learned locomotion policies for legged robots even when average task performance is high, in part because standard curricula primarily adapt task difficulty rather than the distribution of safety-critical experience. We introduce Predictive Safety Curri…

• ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning

  Nico Bohlinger, Jan Peters · 30 de septiembre de 2026

  A goal that is close in space can be far away in time. Obstacles, terrain, and the agent's own capabilities determine how long it takes to get there. Yet, critics in contrastive and survival reinforcement learning do not measure the distances in their representation space in units of time. We theref…

• DAWN: Noise-Robust Quadruped Parkour via Depth-Denoising World Models

  Yohan Choi, Min-Jun Kim, Jin-Sung Kim, Yong-Jae Kim, Youn-Hee Han · 25 de septiembre de 2026

  Vision-based legged locomotion methods assume clean depth at training time and rely on hand-tuned post-processing filters at deployment. However, filter parameters are rarely disclosed, hindering reproducibility, and performance degrades substantially when depth noise is left unaddressed. Building n…

• ForgetMimic: Motion Unlearning for Reinforcement Learning Humanoid Control

  Xukun Luan, Zhongxiang Lei, Chen Gong, Shaowei Li, Yuanguo Bi, Jinyan Liu · 24 de septiembre de 2026

  Humanoid control, leveraging human demonstrations, has achieved diverse, agile, and natural locomotion behaviors through reinforcement learning (RL). While this paradigm has yielded remarkable performance in physical humanoid control, how to eliminate specific motions from learned policies remains i…

• FootQuery: Future-Touchdown-Guided Retrieval from Depth History for Perceptive Humanoid Locomotion

  Tao Dong, Jia Yu, Yuxuan Fan, Linna Zhao, Jiaqi Gong, Andong Yang, Chao Gao, Guyue Zhou · 21 de septiembre de 2026

  Humanoid locomotion over complex terrain requires anticipating footholds that may no longer be visible at touchdown. Limited camera coverage and self-occlusion make it necessary to retrieve relevant terrain information from earlier observations. We present FootQuery, a perceptive locomotion framewor…

• GLAMDRING: Gait Learning And Morphology co-Design via Reinforcement LearnING of CPGs

  Amogh Joshi, Kaushik Roy · 18 de septiembre de 2026

  Robots are moving out of the structured factory floor and into unstructured environments such as disaster sites, planetary surfaces, and agricultural fields, for which the right robot often does not yet exist. We present GLAMDRING, a framework that synthesizes the optimal robot for a locomotion task…

• OmniMimic: Dynamics-completed Motion Augmentation for Multi-style Omnidirectional Quadruped Locomotion

  Sheng Wu, Guoqiang Zhao, Zhe Yang, Fei Teng, Zhikun Zhou, Yanlin Yang, Zheng Fang, Hong Zheng, Yaonan Wang, Kailun Yang · 18 de septiembre de 2026

  Animal demonstrations provide quadruped robots with natural and distinctive gait styles that are difficult to specify through hand-crafted rewards. However, their narrow directional coverage leaves little style-consistent supervision for backward, lateral, and turning commands. We present OmniMimic,…

• Accelerating Visual Policy Learning with Sampling-Based Model Predictive Control

  Yilang Liu, Haoxiang You, Qian Wang, Daniel Rakita, Ian Abraham · 18 de septiembre de 2026

  Learning visual policies for locomotion and manipulation requires coordinating contact with the environment and can incur substantial computation and GPU memory costs. First-order policy gradients (FoPG) reduce training cost through differentiable simulation, but local optimization can converge to u…

• ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC

  Zhenfeng Gan, Yanbo Chen, Lirong Che, Junbo Tan, Xueqian Wang · 14 de septiembre de 2026

  In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered building interiors. Reliable autonomy remains challenging because robot-terrain interaction (RTI) is hybrid and discontinuous, and effective flipper-t…

• Decentralized Evolution of Hexapod Gaits with Independent Leg Controllers

  Gary B. Parker, John Asaro, Jim O'Connor · 14 de septiembre de 2026

  This paper presents a novel approach to hexapod locomotion by evolving each leg's gait independently through a decentralized evolutionary algorithm. Using the Webots simulator and the Mantis hexapod robot, we optimize individual leg controllers without centralized coordination, allowing emergent beh…

• Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion

  Jian Zhou, Xingyu Zhang, Rui Ma, Yu Cao, Shane Xie, Zhi-qiang Zhang · 11 de septiembre de 2026

  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. To address this limitation, w…

• From LLM-Generated Specifications to Learned Quadruped Locomotion

  Merve Atasever, Keyan Azbijari, Cagan Bakirci, Alfredo Reina Corona, Tolga Izdas, Richard Yang, Erdem Biyik, Jyotirmoy V. Deshmukh · 9 de septiembre de 2026

  Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. Designing reward functions requires substantial manual engineering, and it is often unclear which local rewards will induce the desired global behavior. …

• Mind the Phase: Effective Rank and Representation Health in Legged Locomotion

  Felipe Tommaselli, Thiago H. Segreto, Juliano D. Negri, Ricardo V. Godoy, Marcelo Becker · 9 de septiembre de 2026

  Reinforcement learning has become the leading paradigm in legged locomotion, enabling complex behaviors from backflips to parkour through massively parallel simulation. Under PPO's non-stationarity, shallow networks remain the de facto architecture, supported by carefully staged curricula and enviro…

• BRIDGE: An Open-Source Humanoid Platform via Morphology-Control Co-Design for Physical AI

  Jianren Wang, Letian Qian, Zikai Wang, Weiwei Wu, Junjie Zong, Abhinav Gupta, Deepak Pathak · 4 de septiembre de 2026

  Developing humanoid robots capable of leveraging human behavioral data is essential for general-purpose embodiment, yet conventional development remains bottlenecked by a decoupled paradigm that isolates hardware design from whole-body control. This approach leads to suboptimal systems that compromi…

• GigaBrain-WBC-0.5: A Behavior World Model for Robust Whole-Body Control with Environment Interaction

  Ziyang Cheng, Tianshu Tang, Jinxin Lan, Xinze Chen, Yuhan Gong, Zhichao Liu, Changzhong Wu, Yahao Mao, Zongyan Deng, Mingxuan Ma, Huasen Xi, Yilong Liu, Yutong Wu, Xiaofeng Wang, Yang Wang, Yun Ye, Guan Huang, Xiaojie Jin, Zheng Zhu, Jiwen Lu · 20 de agosto de 2026

  Whole-body motion tracking policies turn a humanoid into a robust control interface: the teleoperator---or an upstream model---only supplies a coarse movement intent, while the low-level policy keeps the robot balanced and physically feasible. Existing trackers deliver this interface only on flat gr…

• HAF: Adapting Generalist VLAs to Humanoid Whole-Body Loco-manipulation via Hierarchical Action Flow and Spectral Latent RL

  Langzhe Gu, Chengkai Hou, Meng Li, Xinhua Wang, Jiaming Liu, Xinyuan Lv, Bowei Zhang, Shuanghao Bai, Guangrun Li, Jingyang He, Gaole Dai, Ziluo Ding, Zhiyuan Xu, Kuan Cheng, Jian Tang, Zhengping Che, Shanghang Zhang · 18 de agosto de 2026

  Humanoid robots hold great promise as general-purpose agents in human-centered environments, yet generalist vision-language-action (VLA) foundation models are not readily applicable to humanoid whole-body loco-manipulation. The high dimensionality and interdependence of humanoid motions make it chal…

• Trajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain

  Rocky Liu, Tengyu Liu, Baoxiong Jia, Fangwei Zhong, Xinyi Tong, Hongzhao Xie, Siyuan Huang · 18 de agosto de 2026

  Training locomotion policies for complex unstructured terrain requires a curriculum to avoid early exploration failures. However, since unstructured terrain lacks explicit difficulty ordering for curriculum design, existing methods resort to heuristic curricula over parameterized terrains. This abst…

• Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL

  Martin Schuck, Maks Sorokin, Simone Manni, Duy Ta, Angela P. Schoellig, Marco Hutter, Simon Le Cleac'H, Jan Br\"udigam · 13 de agosto de 2026

  Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely bottlenecked by the slow, manual process of dense reward shaping. To bypass this limitation, we leverage Sample-based Model Predictive Control (SMPC)…

• LUCID: Latent-Skill Unified Control via Imagined Dynamics for Long-Horizon Humanoid Loco-Manipulation

  Cheng Guo, Mingzhe Ni, Angelo Cangelosi, Arash Ajoudani · 11 de agosto de 2026

  Long-horizon humanoid loco-manipulation requires composing versatile whole-body skills and reliable high-level decision making. Existing methods often coordinate pretrained skills with scripted planners, finite-state machines or task-specific model-free policies, restricting their ability to handle …

• Learning Fault-Tolerant Locomotion with Adaptive Gait Timing

  Giovanbattista Gravina, Luca Rossini, Carlo Rizzardo, Arturo Laurenzi, Nikos Tsagarakis · 10 de agosto de 2026

  Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility. This is particularly challenging for larger quadrupeds, where increased mass and tighter actuation limits reduce the feasibility of aggressive, high-frequency compensation s…

• TRACE: Learned Proprioceptive Odometry for Legged Robots under Unreliable Contact Conditions

  Taehyeon Kong, Woojin Kim, Jemin Hwangbo · 7 de agosto de 2026

  In this paper, we present TRACE (Tokenized Robust Attention for Contact-Aware Estimation), an end-to-end learned proprioceptive odometry estimator for legged robots under unreliable contact conditions. The proposed estimator directly predicts relative displacement, relative rotation, and body-frame …

• Nonvisual Classification of Ground-Condition by Artificial Proprioception in an Amoeba-Inspired Autonomous Walking Robot

  Hyoto Yamaguchi, Zenji Yatabe, Seiya Kasai · 7 de agosto de 2026

  Nonvisual classification of ground condition based on a multimodal sensing approach was investigated for an amoeba-inspired autonomous walking robot. To classify ground condition without image sensing and processing, we implemented artificial proprioception by integrating a three-axis accelerometer,…

• Open-DiffLoco: Open-Source Differentiable Learning for Deployable Blind Quadruped Locomotion

  Martin Opat · 4 de agosto de 2026

  Developing deployable locomotion policies through conventional reinforcement learning often requires complex reward engineering and expensive training times. While differentiable simulation offers a highly efficient alternative, open-source tools capable of end-to-end transfer of these policies to p…

• RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking

  Zhenguo Sun, Yibo Peng, Yuan Meng, Xukun Li, Bo-Sheng Huang, Zhenshan Bing, Xinlong Wang, Alois Knoll · 4 de agosto de 2026

  Long-horizon, high-dynamic motion tracking on humanoids remains brittle: retargeted reference motions are typically kinematically plausible but dynamically inconsistent with the robot, so small tracking errors accumulate and eventually destabilize control. We present RobotDancing, a practical single…