6 papers
Position: Vision-Language-Action Models Cannot Be Verified to Perform Physical Reasoning
Taozhao Chen, Ian Manchester, Huaming Chen
Vision-Language-Action (VLA) systems, built on pretrained vision-language models (VLMs), have shown rapidly improving performance on robot manipulation benchmarks. These gains are…
Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking
Ming Yang, Tao Yu, Feng Li +1
Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity. Training such models from scratch re…
Beyond Pixels: Learning Invariant Rewards for Real-World Robotics From a Few Demonstrations
Tengye Xu, Yangting Sun, Ziju Shen +5
Designing reward functions that generalize beyond controlled laboratory settings remains a fundamental challenge in reinforcement learning for robotics. In open-world manipulation…
Latent Action Reparameterization for Efficient Agent Inference
Wenhao Huang, Qingwen Zeng, Qiyue Chen +11
Large language model (LLM) agents often rely on long sequences of low-level textual actions, resulting in large effective decision horizons and high inference cost. While prior wor…
Feature-Selective Representation Misdirection for Machine Unlearning
Taozhao Chen, Linghan Huang, Kim-Kwang Raymond Choo +1
As large language models (LLMs) are increasingly adopted in safety-critical and regulated sectors, the retention of sensitive or prohibited knowledge introduces escalating risks, r…
Trust in LLM-controlled Robotics: a Survey of Security Threats, Defenses and Challenges
Xinyu Huang, Shyam Karthick V B, Taozhao Chen +5
The integration of Large Language Models (LLMs) into robotics has revolutionized their ability to interpret complex human commands and execute sophisticated tasks. However, such pa…