Uni-Mo generates 7,488 language-annotated quadruped motions via LLM prompts and video diffusion, lifts them to 3D trajectories, and trains policies achieving 96.7% real-robot success on 392 sampled motions.
hub Mixed citations
Mujoco: A physics en- gine for model-based control, in: 2012 IEEE/RSJ International Con- ference on Intelligent Robots and Systems, IEEE
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representative citing papers
MPC-Injection biases off-policy RL locomotion policies toward controller-induced behavior basins by injecting MPC transitions into the replay buffer.
HARBOR is a new agentic harness framework that automates robot RL workflows end-to-end across 16 tasks in manipulation, locomotion, and dexterous control, matching or exceeding default configurations while enabling sim-to-real transfer.
SceneCode compiles natural language prompts into executable code programs that generate editable, articulated indoor scenes for physics simulation.
RAT reformulates regularized natural policy gradients as vanilla gradients with a transformed advantage, computed efficiently via randomized block Kaczmarz iterations on on-policy data.
CelloCut formulates watertight remeshing as binary labeling on a Delaunay tetrahedral partition solved by graph-cut minimization with one-sided constraints to guarantee volumetrically consistent solids.
EgoFun3D creates a new task, 271-video dataset, and pipeline using function templates to model interactive 3D objects from egocentric videos for simulation.
HeiSD delivers up to 2.45x faster inference for embodied VLA models by hybridizing speculative decoding with kinematic boundary detection and error-mitigation tricks while preserving task success rates.
Distributed low-resolution time-of-flight sensors along a 53 cm continuum robot, fused with a shape prior, achieve 2.5 cm position and 7.2 degree orientation localization error in simulation and real experiments across multiple environments.
GLUE orchestrates frozen pre-trained generative models into a system-level design generator that enforces feasibility, performance, and diversity, with data-driven and data-free variants benchmarked on UAV design.
BeyondMimic combines compact motion tracking with a unified guided latent diffusion model to master diverse agile behaviors from human demos and solve unseen downstream tasks via test-time classifier guidance.
LLMPhy uses iterative LLM-generated programs executed in physics engines to solve continuous parameter estimation and discrete scene layout problems, outperforming prior black-box methods on three new zero-shot physical reasoning datasets.
Releases the largest open teleoperation dataset for robot manipulation together with hardware, simulation, and training infrastructure to support scalable behavior cloning.
Hallucination in world models is a data coverage issue predictable by three signals and preventable through targeted training sampling and online data collection.
OmniContact introduces contact flow as a shared representation of body trajectories and contact signals to learn and chain loco-manipulation meta-skills, reporting 98.7% success on box carrying and 76.5% on push-stack tasks.
AutoDex automates the full perception-execution-labeling-reset loop for real-world dexterous grasping data collection, delivering 4.8x throughput over teleoperation and 76% success for retrieved grasps versus 34% from simulation-only data.
NASDAQ normalizes observations in an online RL setting so that dynamics prediction losses are balanced across dimensions, yielding competitive performance with lower wall-time than prior model-based and self-predictive methods.
The paper introduces an inductive generalization evaluation protocol for manipulation policies and shows that SOTA vision-language-action models fail on progressively harder task variants.
DO AS I DO reconstructs and retargets hand-object interactions from in-the-wild monocular RGB videos to produce dexterous robot manipulation trajectories, outperforming prior methods on ground-truth and online video datasets.
AnnotateAnything converts passive 3D assets into manipulation-ready assets by combining vision-language reasoning for semantics with parallel physics pipelines for executable action annotations such as grasps and articulations.
A post-hoc predictive safety filter adjusts RL policy contact locations for quadruped robots via sampling-based optimization on a full-physics model, reducing safety violations in cluttered environments with minimal performance deviation.
AEGIS uses activation probes for early-warning detection of high-risk steps in weak policies and selectively escalates to stronger policies, recovering 10.1% of lost trajectories on LIBERO-Spatial while activating the strong policy on only 38% of steps.
Trains embodiment-aware value functions on up to 50 robots and applies their gradients as differentiable surrogates to optimize held-out robot designs with over 1100 parameters.
A VAE-based latent task representation enables automatic curriculum generation in CRL for non-Euclidean navigation tasks, outperforming interpolation and GAN-based methods in experiments.
citing papers explorer
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GLUE: Coordinating Pre-Trained Generative Models for System-Level Design
GLUE orchestrates frozen pre-trained generative models into a system-level design generator that enforces feasibility, performance, and diversity, with data-driven and data-free variants benchmarked on UAV design.