This study introduces a novel control framework for adaptive cruise control (ACC) in automated driving, leveraging neural networks and physics-informed constraints. As automated vehicles (AVs) adopt advanced features like ACC, transportation systems are becoming increasingly intelligent and efficient. However, existing...
Yuhui Liu, Samannita Halder, Shian Wang et al.· 0 citations
AntiGrounding, a visual action-selection framework built around a dual geometric--visual trajectory interface, is presented, where component ablations and evaluator sensitivity characterize trajectory assessment, proposal search, orientation control, and evaluator choice.
Wen-Bo Li, Yi-Teng Chen, Wen-Hao Li et al.· 0 citations
Coding agents have emerged as a promising paradigm for robot manipulation: a language model writes the robot controller as a program, and agents built in this way now operate robots without robot-specific training.Whether this paradigm is also safe, however, has not been asked. We evaluate coding agent under a safety c...
Bing-Xin Xu, Yu-Zhang Shang, Zhen-Han Dong et al.· 0 citations
Complex robotic manipulation tasks frequently require a long-term memory of past events and actions. As conditioning on full histories renders policies prone to spurious correlations and degrades performance, many approaches to policy memory involve compressing historical information through expensive VLM queries in-th...
Nitish Dashora, Douglas Chen, Idan Shenfeld et al.· 0 citations
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Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence. Most feed-forward methods infer articulation from a single observation and therefore rely heavily on learned category-level shape priors. We present FAMOS, a feed-forward m...
Kevin Qu, Tao Sun, Massimiliano Viola et al.· 0 citations
Action chunking is widely used for action generation and execution in Vision-Language-Action (VLA) policies, yet existing approaches commonly use a fixed action horizon. During a rollout, different task stages may require different levels of action continuity, control precision, and closed-loop feedback, making a fixed...
Xin Chen, Sen Chen, Yujuan Ding et al.· 0 citations
Large-scale vision-language-action (VLA) models provide powerful priors for robot manipulation, yet adapting them to a specific deployment remains challenging. Supervised fine-tuning (SFT) on task-specific demonstrations provides a step toward deployment, but faces two persistent limitations: static data provide limite...
Zimu Han, Yi-Ming Zeng, Ji-Yao Zhang et al.· 0 citations
Autonomous underwater vehicles (AUVs) operating beyond reliable communications must recover from failures without human intervention. We investigate an architecture in which conventional deterministic layered control autonomy manages normal operations, while an invokable large language model (LLM) serves as a diagnosti...
Khalid Halba, Kylie Cooper, James G. Bellingham· 0 citations
Sampling-Guided Policy Search (SGPS), which couples recurring action-target refinement by sampling-based model-predictive control with first-order policy optimization with first-order policy optimization, and shows that refinement improves policy learning beyond initialization and tracking alone.
Yi-Lang Liu, Hao-Xiang You, Qian Wang et al.· 0 citations
An automated pipeline is presented that transforms safe driving scenes into safety-critical scenarios by combining computer vision, Large Language Models (LLMs), and Augmented Reality (AR) to support the testing and validation of autonomous driving systems.
Noura Fady, Farah Khaled, Catherine M. Elias· 0 citations
VLN on the Fly, an onboard stack that keeps grounding, planning, and control as separate, inspectable stages that reaches the target in 13 of 15 trials with 5.72 cm mean goal error and 39.3% average GPU utilization.
Marco S. Tayar, F. Tommaselli, Gianluca Capezutto et al.· 0 citations
Astronex-World 1.0 is presented, an open controllable video world-model foundation that provides a bidirectional model for full-context generation and a causal model with block-causal attention and cross-block KV caching for persistent generation, both built on the Wan2.2-TI2V-5B prior.
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.
Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic applications.
From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks.
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