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
Mixed citation behavior. Most common role is background (67%).
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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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Closed-Loop Sim-to-Real Reinforcement Learning for Deformable Microfiber Shape Control
A closed-loop sim-to-real RL policy trained in a simplified frictionless simulator achieves sub-millimeter microfiber shape control on physical hardware via visual feedback without retraining.
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SmoCap: Unified Scale-Pose Canonicalization with Proxy-Mapped Trust-Region QP
SmoCap performs unified scale-pose canonicalization for motion capture by solving constrained trust-region QPs with analytical proxy-mapped Jacobians in a sparse control subspace.
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Automatically Improving Simulation Physics for Articulated Objects
A simulator-in-the-loop multi-modal method refines physical properties of incomplete 3D articulated objects to improve simulation stability and downstream robot policy performance.
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Before the Body Moves: Learning Anticipatory Joint Intent for Language-Conditioned Humanoid Control
DAJI is a hierarchical framework using distillation and autoregressive generation to learn future-aware joint intents for language-conditioned humanoid robot control.
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Rethinking Priority Scheduling for Sequential Multi-Agent Decision Making in Stackelberg Games
HPA dynamically selects agent decision orders in Stackelberg games to improve equilibria and performance in multi-agent MuJoCo control tasks.
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Gated Memory Policy
GMP selectively activates and represents memory via a gate and lightweight cross-attention, yielding 30.1% higher success on non-Markovian robotic tasks while staying competitive on Markovian ones.
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ComSim: Building Scalable Real-World Robot Data Generation via Compositional Simulation
Compositional Simulation generates scalable real-world robot training data by combining classical simulation with neural simulation in a closed-loop real-sim-real augmentation pipeline.
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From Fold to Function: Simulation-Driven Design of Origami Mechanisms
A simulation framework using MuJoCo deformable bodies and CMA-ES optimization enables rapid design and experimental validation of origami mechanisms like an improved catapult.
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MOBIUS: A Multi-Modal Bipedal Robot that can Walk, Crawl, Climb, and Roll
MOBIUS is a multi-modal bipedal robot with hybrid reinforcement learning and force control plus an MIQCP planner that enables walking, crawling, climbing, and rolling on varied terrains.
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Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension
Neural regression collapse occurs when last-layer feature intrinsic dimension falls below target intrinsic dimension, creating over-compressed and under-compressed regimes that govern generalization based on data quantity and noise.
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Behavior Synthesis via Contact-Aware Fisher Information Maximization
Derives a contact-aware Fisher information measure to synthesize robot behaviors that maximize information-rich contacts for efficient object parameter learning.
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Gymnasium: A Standard Interface for Reinforcement Learning Environments
Gymnasium establishes a standardized API for RL environments to improve interoperability, reproducibility, and ease of development in reinforcement learning.
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Latent Linear Quadratic Regulator for Robotic Control Tasks
LaLQR learns a latent linear-quadratic representation of robotic systems by imitating MPC to enable efficient LQR control.
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Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning
Human2Any transfers human video demonstrations to robots by representing tasks as object-object interactions and composing learned priors with robot-side planning.
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A Scalable Embodied Intelligence Platform for Seamless Real-to-Sim-to-Real Transfer of Household Mobile Manipulation Tasks
BestMan is a robotics platform with ASG for scene reconstruction, simulation-guided skill learning, and HUM middleware to enable seamless real-to-sim-to-real transfer in household mobile manipulation.
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TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion
A multi-channel terrain affordance reward combined with lower-body compliance training via virtual wrenches enables end-to-end PPO-trained humanoid policies to walk at 1 m/s on 0.2 m risers with improved payload robustness.
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Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems
A literature review that defines silent physical-action failures in Physical AI and identifies the lack of complete runtime authorization boundaries across surveyed technical streams.
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Distilling Game Code World Model Generation into Lightweight Large Language Models
SFT followed by RLVR on Qwen2.5-3B-Instruct raises syntactic and execution correctness when generating Game Code World Models across 30 games.
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Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters
A SHAP analysis framework is introduced to decompose configuration impacts on RL generalization and guide selection for improved performance in robotics.
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The embodied brain: Bridging the brain, body, and behavior with neuromechanical digital twins
Neuromechanical digital twins embed neural controllers in simulated bodies to infer unmeasurable biophysical variables, generate testable hypotheses via perturbations, and bridge neuroscience with robotics and machine learning.
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