Large Reasoning Language Models (LRLMs) enable multi-step reasoning for robotic task planning, but continued reasoning can overwrite valid intermediate plans or leave constraint violations unresolved, reducing planning reliability and wasting inference-time computation. We develop an inference-time monitor that exposes...
Wei-Zhe Xu, Jia-Liang Fan, Meng-Yu Liu et al.· 0 citations
Modern data-driven decision-making methods, such as imitation learning (IL) and reinforcement learning (RL), have achieved great success in solving many complex tasks. However, these methods often suffer from serious control instability and robustness issues when applied in real-world applications such as robotics and...
Peng Cheng, Yun-Xian Hou, Zhi Zhou et al.· 0 citations
Robotic manipulation often contains repeated motions whose local observations look similar at different phases. When these phases require different actions, a policy that relies mainly on the current observation may repeat completed motions or switch phases at the wrong time. To address this phase ambiguity, we present...
Changchuan Yang, Haoxuan Xu, Wenbo Chen et al.· 0 citations
Agentic AI based on Large Language Model generalization capabilities offers a wide range of potential applications, including planning for embodied tasks. For example, embodied agents based on Foundation models can generate plausible plans in autonomous robotics scenarios. Due to limited context windows or hallucinator...
Vincenzo Guarino, Emanuele Musumeci, Vincenzo Suriani et al.· 0 citations
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NavGPT-3 is presented, a harness that connects the two models, with an OS-like runtime built above it: reasoning, acting, and monitoring run as threads with their own context, tools, and permissions, while the runtime schedules them and decides which thread controls the robot's motion, so that the robot can react to su...
Geng-Ze Zhou, Yicong Hong, Jiazhao Zhang et al.· 0 citations
Generative motion planners typically use learned trajectory priors for initial generation, while leaving test-time repair to local continuous refinement. We introduce Masked Generative Motion Planning (MGMP), which extends the learned prior from efficient parallel generation to structural repair. A masked generative tr...
Li-Peng Zhuang, Ying-Dong Ru, Shi-Yu Fan et al.· 0 citations
Behavior cloning (BC) in non-Markovian environments is a challenging problem because policies have to reason over contextual information over long horizons. Existing policy architectures rely on recurrent or attention-based mechanisms to capture long-term dependencies. However, recurrent models suffer from hidden-state...
Instant delivery, shipping items before critical deadlines, is essential in daily life. While multiple delivery agents, such as couriers, Unmanned Aerial Vehicles (UAVs), and crowdsourced agents, have been widely employed, each of them faces inherent limitations (e.g., low efficiency/labor shortages, flight control, an...
Junhui Gao, Yan Pan, Qianru Wang et al.· 0 citations
Robotic systems generate large volumes of multimodal sensor data, but converting ROS bag recordings into machine learning datasets is often handled by ad hoc sequential scripts, creating engineering overhead and slow iteration cycles. We model dataset construction as an artifact-based build process over a dependency gr...
Leon Pohl, Lukas Beer, George Sebastian et al.· 0 citations
The sensitivity to instruction phrasing is characterized with statistically tested single-edit swings and an oracle phrase search, which shows that phrasing alone nearly closes the 21-point gap between in-distribution and out-of-distribution tasks.
General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carry into the physical world. To investigate this, we introduce RobotWorld, a challenging simulation testbed for robot use: turning instructions and observations into physic...
Zhiqin Yang, Chen-Xin Li, Xiao-Meng Hu et al.· 2 citations
The sharp bound of $n(n-1)$ vertices for $n\ge2$ is proved, answering Rote's rotation-assignment open problem, and assignment-based bounds extend the approach to three-dimensional rotations and partial matching at a supplied translation through branch-and-bound.
Subhransu S. Bhattacharjee, Dylan Campbell, Rahul Shome· 0 citations
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
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.
