10 papers
Constrained Differentiable Policy Learning for Safe Quadrotor Navigation
Yan Rui Tan, Praveen Krishnapur, Zeyu Feng +3
Differentiable simulation enables efficient policy optimization through analytic gradients, yet safety is commonly incorporated as a weighted penalty, requiring manual tuning of th…
Using large language models for embodied planning introduces systematic safety risks
Tao Zhang, Kaixian Qu, Zhibin Li +4
Large language models are increasingly used as planners for robotic systems, yet how safely they plan remains an open question. To evaluate safe planning systematically, we introdu…
Safe Navigation in Unknown and Cluttered Environments via Direction-Aware Convex Free-Region Generation
Zhicheng Song, Yongjian Li, Kai Chen +3
Convex free regions provide a structured and optimization-friendly representation of collision-free space for robot navigation in unknown and cluttered environments. However, exist…
FlowHOI: Flow-based Semantics-Grounded Generation of Hand-Object Interactions for Dexterous Robot Manipulation
Huajian Zeng, Lingyun Chen, Jiaqi Yang +4
Recent vision-language-action (VLA) models can generate plausible end-effector motions, yet they often fail in long-horizon, contact-rich tasks because the underlying hand-object i…
Fast and Safe Trajectory Optimization for Mobile Manipulators With Neural Configuration Space Distance Field
Yulin Li, Zhiyuan Song, Yiming Li +8
Mobile manipulators promise agile, long-horizon behavior by coordinating base and arm motion, yet whole-body trajectory optimization in cluttered, confined spaces remains difficult…
GO-Flock: Goal-Oriented Flocking in 3D Unknown Environments with Depth Maps
Yan Rui Tan, Wenqi Liu, Wai Lun Leong +4
Artificial Potential Field (APF) methods are widely used for reactive flocking control, but they often suffer from challenges such as deadlocks and local minima, especially in the…
Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids
Arturo Flores Alvarez, Fatemeh Zargarbashi, Havel Liu +7
We present a Reinforcement Learning (RL)-based locomotion system for Cosmo, a custom-built humanoid robot designed for entertainment applications. Unlike traditional humanoids, ent…
Learning Quiet Walking for a Small Home Robot
Ryo Watanabe, Takahiro Miki, Fan Shi +5
As home robotics gains traction, robots are increasingly integrated into households, offering companionship and assistance. Quadruped robots, particularly those resembling dogs, ha…
Residual Policy Learning for Perceptive Quadruped Control Using Differentiable Simulation
Jing Yuan Luo, Yunlong Song, Victor Klemm +3
First-order Policy Gradient (FoPG) algorithms such as Backpropagation through Time and Analytical Policy Gradients leverage local simulation physics to accelerate policy search, si…
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers
Fan Shi, Chong Zhang, Takahiro Miki +3
Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL). Controllers employing neur…