Category
1,183 papers
The results establish that simulation-screened task semantics can effectively amortize control into robust policies, without demonstrations or manual dense rewards, unifying symbolic planning and data-driven execution.
LightNav-0 is presented, a compact generalist embodied navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads, and establishes compact VLMs as a unified and transferable backbone for generalist embodied navigation.
Every valid trajectory can teach what physically happened, while the actor is deployed only under the condition associated with relatively better actions, in \method, a direct world-action policy that combines outcome-agnostic predictive learning with outcome-aware policy improvement.
CorrectVLA is presented, a framework that translates task-level natural language corrections into additive action magnitude adjustments without modifying policy weights, and succeeds when policies possess strategic correctness and fails when fundamental comprehension is absent, establishing a practical operational boun...
Reach audiences
Advertise in front of researchers, engineers, and readers.
Achieve-Grounded Memory is proposed, a lightweight closed-loop framework for frozen VLA policies that represents a task as a subgoal sequence with a progress pointer and advances this memory only after the current subgoal is verified by physical evidence.
A new diffusion method for multi-agent planning with STL specifications is introduced, making the approach generalizable to novel formulas whose predicates are placed anywhere within the goal region covered during training, while achieving the same scalability as existing learning-based methods.
Semantically-Guided Exploration (SGE) is introduced, a modular exploration framework for ground vehicles that integrates pixel-level semantic segmentation into sampling-based waypoint selection and receding-horizon route optimization and introduces mechanisms to address real-world navigation uncertainty.
Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances in computer vision and deep learning have enabled detailed analysis of pollinator behaviour, but monitoring must trade-off detail against spatial coverage and human or tech...
Object detectors are typically trained under closed-set supervision, where unlabeled regions are implicitly treated as background. Under incomplete annotations, this assumption introduces objectness bias: visually valid but unlabeled objects are used as negatives, tying objectness to the annotated taxonomy rather than...
CGFM-Nav is introduced, a foundation-model-based framework for lifelong multimodal navigation that integrates task-relevant subgraph selection, VLM reasoning, and verification feedback into a closed decision loop, and preliminary experiments show that CGFM-Nav improves the overall success rate, demonstrating the effect...
RoboPhys-3D, a 3D-grounded EWM benchmark built on RoboTwin 2.0, is introduced, and Average Full Score, a hierarchical score averaging all 50 metrics for comprehensive evaluation, and RoboPhyscore, a compact task-aligned score averaging the metrics most strongly correlated with task success are introduced.
NavMCP is introduced, an agentic scaffolding framework that couples a VLM reasoning agent with an NFM executor for long-horizon exploration that achieves state-of-the-art results on HM-EQA, MT-HM3D, and EXPRESS-Bench.
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.
