Category
1,183 papers
It is argued that the composition of independent sub-policies deserves to be treated as a research problem in its own right, rather than as an implementation detail left to whatever mechanism happens to be at hand.
High-mix low-volume (HMLV) manufacturing requires inspection systems to adapt to changing parts, specifications, and work orders without repeated task-specific programming. Existing inspection automation typically assumes predefined sensing sequences, while general purpose robot agents optimize task completion rather t...
A threshold-function reformulation is introduced in which a lower-closed set on a d-dimensional grid is represented by its column heights along a designated axis and reformulates the greatest-fixed-point iteration as independent one-dimensional binary searches, yielding an embarrassingly parallel iteration with asympto...
Completeness-aware Motion Correspondence (CMC), a ground-truth-anchored evaluation protocol that jointly measures localization, trajectory completeness, visibility, and continuity, counting missing predictions as failures on visible dynamic points, is introduced.
Reach audiences
Advertise in front of researchers, engineers, and readers.
IMPLIED is an implication modeling method that treats fixed-rule implications as an initial guide while learning to infer and revise accepted and rejected action labels over time, and predicts human implications more accurately than the fixed rule approach and LLM baselines, approaching the performance of a human-label...
ReWeight is introduced, a framework that incorporates human data into VLA post-training through demonstration-level retrieval and sample-level weighting and learns a cross-embodiment visuomotor representation that combines visual observations with future actions to measure behavioral similarity between human and robot...
LePlanner is an amortized iterative controller that learns to construct and refine latent action sequences through a frozen world-model predictor, enabling fast, horizon-aware, nonlinear physical control without online optimization.
Increasing conjunction frequency in low Earth orbit places growing pressure on spacecraft operators to determine not only whether an encounter requires mitigation, but whether sufficient information is available to commit to a maneuver. This work formulates this information-action tradeoff as a belief-space planning pr...
Large Language Models (LLMs) have enabled more natural human-robot interaction, but open-source models often exhibit unstable long-horizon reasoning and inefficient action execution when deployed in agentic robotic frameworks. This paper presents an enhanced ROS-Agent based architecture that improves task reliability a...
A data-driven state PRedictor-based Interacting Multiple Model tracking method (PR-IMM) that improves nonlinear object-motion representation while preserving the stability and interpretability of physics-based motion models.
A conflict-predictive variable horizon that each drone sets locally, leaving the distributed model predictive control itself unchanged, is proposed, and it is proved that recursive feasibility and asymptotic stability are preserved for every horizon the policy can select.
ShieldVLA is proposed, a safety-aligned fine-tuning framework for VLA models based on Hamilton-Jacobi (HJ) reachability that reduces cumulative safety cost by 57% on average and improves task success rate by +0.13 over SafeVLA.
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.
A Blog post by Amazon on Hugging Face
Director of CSAIL and MIT professor honored for her contributions to robotics, artificial intelligence, and autonomous systems.
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
