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Challenging Locomotion

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Rapid co-design of Buoyancy-assisted robots for Challenging Locomotion using Gaussian Evolutionary Specialists

new Abstract: Designing high-performance legged robots requires jointly optimizing morphology and control. Model-free Reinforcement Learning (RL) offers an alternative to model-predictive control for developing robust controllers without explicitly specifying robot dynamics. Thus, we have seen theuse of RL to train controllers and evaluate designs for robot morphology optimization.

arXiv CS 2d ago

TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion

arXiv:2606.05880v1 Announce Type: new Abstract: Agile humanoid locomotion across diverse challenging terrain demands both wide perceptual coverage and precise local geometry understanding. Motivated by the way humans selectively look at relevant terrain during locomotion, we introduce TAGA, a Terrain-aware Active Gaze learning framework for Attention-based humanoid control. By fusing vision, proprioception, and motion commands, our framework guides the model to learn anticipatory cues and...

arXiv CS 5d ago

T-GMP: Terrain-conditioned Generative Motion Priors for Versatile and Natural Humanoid Locomotion

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arXiv CS 2d ago

Co-training with Ego-centric Video and Demonstration for Robot Navigation Task

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arXiv CS 8d ago

Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer

arXiv:2512.24698v2 Announce Type: replace Abstract: Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various legged locomotion tasks, producing highly dynamic behaviors often requires extensive reward tuning or high-quality demonstrations. Leveraging reduced-order models can help mitigate these challenges.

arXiv CS 6d ago

Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking

arXiv:2606.08253v1 Announce Type: new Abstract: Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately. While reinforcement learning with velocity-commanded policies has achieved remarkable robustness in humanoid locomotion, this approach lacks explicit control of the foothold placement, leading to unsafe behavior, such as stepping onto human feet, or imprecise...

arXiv CS 1d ago

CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation

arXiv:2606.04718v1 Announce Type: new Abstract: Humans primarily rely on walking and running to traverse complex terrains, without resorting to unnecessarily complex motion patterns. Similarly, humanoid robots should achieve smooth transitions between walking and running while maintaining natural and stable locomotion. However, unifying gait transition and multi-terrain adaptation within a single policy remains challenging due to gradient interference and the distribution shift induced by...

arXiv CS 6d ago

Coupled Local and Global World Models for Efficient First Order RL

arXiv:2602.06219v2 Announce Type: replace Abstract: World models offer a promising avenue for more faithfully capturing complex dynamics, including contacts and non-rigidity, as well as complex sensory information, such as visual perception, in situations where standard simulators struggle. However, these models are computationally complex to evaluate, posing a challenge for popular RL approaches that have been successfully used with simulators to solve complex locomotion tasks but yet...

arXiv CS 7d ago

SSR: Scaling Surefooted and Symmetric Humanoid Traversal to the Open World

arXiv:2605.30770v1 Announce Type: new Abstract: Extending humanoid traversal to the open world is key to practical deployment in human environments, but remains challenging. The robot must use vision to ensure safe and reliable foot placement on heterogeneous terrain under highly dynamic motion, while producing coordinated, natural whole-body behaviors. We propose SSR, an efficient end-to-end framework for egocentric vision-based humanoid traversal that jointly learns these capabilities.

arXiv CS 9d ago

Predicting and controlling nonlinear neuro-mechanical locomotion dynamics

arXiv:2605.03362v1 Announce Type: cross Abstract: Neuromechanics aims to understand the link between an animal's neural activity and its physical behaviors. Recent advances in experimental and machine learning techniques enable simultaneous recordings of neural and locomotion dynamics over long time periods and across multiple behavioral transitions in worms, flies, and other organisms. These high-dimensional datasets present the challenge of inferring interpretable low-dimensional dynamical...

arXiv Physics 5d ago