1 citations · 1 across the 7 of their papers we have counts for
7 papers
ComFree-Sim: A GPU-Parallelized Analytical Contact Physics Engine for Scalable Contact-Rich Robotics Simulation and Control
Chetan Borse, Zhixian Xie, Wei-Cheng Huang +1
Physics simulation for contact-rich robotics is often bottlenecked by contact resolution: mainstream engines enforce non-penetration and Coulomb friction via complementarity constr…
RoDiF: Robust Direct Fine-Tuning of Diffusion Policies with Corrupted Human Feedback
Amitesh Vatsa, Zhixian Xie, Wanxin Jin
Diffusion policies are a powerful paradigm for robotic control, but fine-tuning them with human preferences is fundamentally challenged by the multi-step structure of the denoising…
Robust Reward Alignment via Hypothesis Space Batch Cutting
Zhixian Xie, Haode Zhang, Yizhe Feng +1
Reward design in reinforcement learning and optimal control is challenging. Preference-based alignment addresses this by enabling agents to learn rewards from ranked trajectory pai…
Language-Model-Assisted Bi-Level Programming for Reward Learning from Internet Videos
Harsh Mahesheka, Zhixian Xie, Zhaoran Wang +1
Learning from Demonstrations, particularly from biological experts like humans and animals, often encounters significant data acquisition challenges. While recent approaches levera…
Safe MPC Alignment with Human Directional Feedback
Zhixian Xie, Wenlong Zhang, Yi Ren +3
In safety-critical robot planning or control, manually specifying safety constraints or learning them from demonstrations can be challenging. In this article, we propose a certifia…
CarbonCP: Carbon-Aware DNN Partitioning with Conformal Prediction for Sustainable Edge Intelligence
Hongyu Ke, Wanxin Jin, Haoxin Wang
This paper presents a solution to address carbon emission mitigation for end-to-end edge computing systems, including the computing at battery-powered edge devices and servers, as…
How Can LLM Guide RL? A Value-Based Approach
Shenao Zhang, Sirui Zheng, Shuqi Ke +6
Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. However, RL algorit…