10 papers
AdaReP:Adaptive Re-Planning under Model Mismatch for Neural World-Model Predictive Control
Yutian Cheng, Xiaojian Ma, Xianhao Wang +6
Neural world models coupled with model predictive control (MPC) replan at every environment step to bound accumulated prediction error, but this incurs substantial computational ov…
Dynamic Whole-Body Dancing with Humanoid Robots -- A Model-Based Control Approach
Shibowen Zhang, Jiayang Wu, Guannan Liu +12
This paper presents an integrated model-based framework for generating and executing dynamic whole-body dance motions on humanoid robots. The framework operates in two stages: offl…
Towards Bridging the Gap between Large-Scale Pretraining and Efficient Finetuning for Humanoid Control
Weidong Huang, Zhehan Li, Hangxin Liu +3
Reinforcement learning (RL) is widely used for humanoid control, with on-policy methods such as Proximal Policy Optimization (PPO) enabling robust training via large-scale parallel…
Integrated Exploration and Sequential Manipulation on Scene Graph with LLM-based Situated Replanning
Heqing Yang, Ziyuan Jiao, Shu Wang +3
In partially known environments, robots must combine exploration to gather information with task planning for efficient execution. To address this challenge, we propose EPoG, an Ex…
ECO: Energy-Constrained Optimization with Reinforcement Learning for Humanoid Walking
Weidong Huang, Jingwen Zhang, Jiongye Li +6
Achieving stable and energy-efficient locomotion is essential for humanoid robots to operate continuously in real-world applications. Existing MPC and RL approaches often rely on e…
ReSPIRe: Informative and Reusable Belief Tree Search for Robot Probabilistic Search and Tracking in Unknown Environments
Kangjie Zhou, Zhaoyang Li, Han Gao +4
Target search and tracking (SAT) is a fundamental problem for various robotic applications such as search and rescue and environmental exploration. This paper proposes an informati…