robotics

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ActionPiece: Rethinking Action Tokenization for Autoregressive Vision-Language-Action Models arxiv.org
Action tokenizers for autoregressive VLA models often fail to preserve subtle action adjustments, despite good pointwise reconstruction. This paper introduces Physical Rank Consistency (PRC) to measure relational fidelity and ActionPiece, a new tokenizer that uses joint supervision to preserve these physical relationships, significantly improving policy success on robotics benchmarks.
Hugging Face Daily Papersarxiv.orgShijie Lian, Bin Yu, Zhaolong Shen6d1 minpaper
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FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations arxiv.org
FAMOS is a feed-forward model that predicts 3D articulation, including movable-part segmentation and joint parameters, from sparse, unordered point clouds. It leverages multiple observations through a Multi-state Articulation Transformer and a novel observed articulation span objective, outperforming existing baselines.
Hugging Face Daily Papersarxiv.orgKevin Qu, Tao Sun, Massimiliano Viola5d1 minpaper
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Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models arxiv.org
Latent Interface Training (LIT) first learns a goal‑conditioned action prior without visual input, then adds a pose‑supervised latent interface as the only visual conditioning path. Applied to several vision‑language‑action models, LIT cuts vision‑action shortcuts and lifts LIBERO‑Plus success by 3.9–10.7 points and real‑world task success by 13.3–16.7 points under distribution shifts.
Hugging Face Daily Papersarxiv.orgJianman Lin, Shailesh Shailesh, Zhongyi Luo11d1 minpaper
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VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control arxiv.org
VA‑Bench is a new benchmark that evaluates general‑purpose multimodal LLMs on the full observe‑reason‑act‑revise loop in embodied robotics, using RGB demonstrations, active camera control, and metric Cartesian commands. The best model reaches 53.9% average task success, showing active perception helps but long‑horizon tasks remain unsolved.
Hugging Face Daily Papersarxiv.orgZhongbo Zhang, Jiayi Jin, Yifan Wang5d1 minpaper
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In-Context Robot Learning with VLM Agents arxiv.org
GPT‑Policy is a framework that lets a large vision‑language model (e.g. GPT‑6 Astra) perform in‑context robot learning: a context compiler extracts visual transitions from demos, the VLM proposes tool actions, and a constrained controller verifies and executes them. Real‑robot experiments show that raw video demos improve success rates even without explicit action labels, and that providing align…
Hugging Face Daily Papersarxiv.orgDongzhou Cheng, Taoran Yi, Ye Fang6d1 minpaper
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GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning arxiv.org
GAVEL augments LLM‑driven robot planners with an explicit graph world model that verifies actions, repairs violations, and reasons over belief distributions, boosting single‑task success from 41 % to 92 % and multi‑task success from 20 % to 93 % on BEHAVIOR‑1K.
Hugging Face Daily Papersarxiv.orgRuiyang Wang, Hao-Lun Hsu, Swarajh Mehta6d1 minpaper
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HarnessVLN: Unifying Training-Free Embodied Navigation through an Agent Harness arxiv.org
HarnessVLN introduces a zero‑shot, training‑free embodied navigation framework that wraps a multimodal LLM in an "Agent Harness" – a tool‑based protocol that validates planner actions against spatial evidence, tracks progress with hierarchical event memory, and maintains a persistent spatiotemporal graph for recovery. The system works for instruction‑following and object‑goal tasks, achieving 60.…
Hugging Face Daily Papersarxiv.orgYang Chen, Lirong Che, Zhenyu Huang8d1 minpaper
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From Pretraining to Proficiency: Real-World Subtask RL for Long-Horizon Manipulation with Minimal Human Intervention arxiv.org
The paper presents PARTS, a framework that augments a frozen pretrained robot policy with RL‑learned residuals on selected bottleneck subtasks, using local success rewards and minimal human resets. In real‑world bimanual and single‑arm tasks, PARTS more than doubles success rates with only minutes of robot rollouts, outperforming prior fine‑tuning methods.
Hugging Face Daily Papersarxiv.orgSichang Su, Benjamin Yang, Zhiyun Deng4d1 minpaper
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Modality-Autoregressive World-Action Models arxiv.org
ModAR is a world‑action model that autoregressively denoises multiple future modalities (depth, DINO features, point tracks) before predicting actions, letting each prediction condition on earlier outputs. It outperforms prior WAMs, achieving higher success rates with ~20× fewer training FLOPs and no pretraining.
Hugging Face Daily Papersarxiv.orgAdam Hung, Bardienus P. Duisterhof, Deva Ramanan7d1 minpaper
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DeformSmith: Physics Harness-Guided Hierarchical Generation of Deformable Assets for Robot Manipulation arxiv.org
DeformSmith is a framework that generates physically plausible deformable assets for robot manipulation from a text prompt or a single image, using a hierarchical construction process guided by a shared physics harness. It outperforms prior baselines in visual fidelity and physical realism while also producing interaction data for downstream tasks.
Hugging Face Daily Papersarxiv.orgCan Li, Jie Gu, Zishun Deng5d1 minpaper
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Learning Foresight without Explicit Trajectories for 3D Diffusion Policies arxiv.org
This paper introduces Movement Trend Guidance (MTG), a method to provide foresight to 3D diffusion policies for robotic manipulation without explicit trajectory planning. MTG learns a compact latent representation of interaction evolution, significantly improving performance on various benchmarks with minimal parameter overhead.
Hugging Face Daily Papersarxiv.orgZhongbo Zhang, Zaibin Zhang, Yifan Wang5d1 minpaper
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REVERSAL-BENCH: A Reversibility Axis and Reset Oracle for Measuring the Reset-Free RL Cliff apple.com
The paper presents REVERSAL‑BENCH, a benchmark that varies environment reversibility with a parameter ρ and provides a ground‑truth reset oracle for eight manipulation tasks. Using it, the authors show that reset‑free RL agents hit a sharp reversibility cliff and become permanently trapped, while episodic agents remain robust.
Apple Machine Learning Researchapple.com5d1 minpaper
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Fingers as Legs: Learning Self-Supported Locomotion and Manipulation with an Anthropomorphic Hand arxiv.org
The authors train an anthropomorphic robotic hand to crawl, steer, and recover from falls using its fingers for both support and manipulation, via a reinforcement‑learning reward formulation tuned to the hand's asymmetry. Sim‑to‑real experiments show faster locomotion than quadruped‑style rewards and successful untethered tasks without onboard vision.
Hugging Face Daily Papersarxiv.orgAmirhossein Kazemipour, Hehui Zheng, Robert Katzschmann7d1 minpaper
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Why Deploying Physical AI at Scale Demands Safety at Every Layer nvidia.com
NVIDIA’s Halos platform is a full‑stack safety system for physical AI (autonomous vehicles and industrial robots). It bundles safety‑engineered hardware (DRIVE AGX Thor, IGX Thor), an ASIL‑D certified OS (Halos OS), middleware for isolation and monitoring, AI models for explainability (Alpamayo), and simulation/validation tools (Isaac Lab, Omniverse). The blog argues that scaling physical AI requ…
Nvidianvidia.comRiccardo Mariani12h5 min