Robotics & Embodied AI

Multibody/Robotics

A robot arm manipulation dataset collected using the LeRobot framework, containing 51 episodes and 29803 frames of video data for robot manipulation policy learning.

Multibody

Proposes specification-conditioned human demonstration collection for embodied AI in construction, bridging the gap between design model configurations and physical environments.

Embodied AI / Robot Foundation Model

Unitree releases UnifoLM-WLA-1.0, a 6B parameter general-purpose embodied AI model trained with ~2500 hours of real-machine data, achieving 7 open-source SOTA results in embodied reasoning.

Multibody

A survey on embedding physics priors in robot learning, covering how physical constraints like contact mechanics, rigid-flexible coupling, and multibody dynamics improve sample efficiency and reliability of learned policies.

Multibody

NVIDIA tutorial demonstrating GPU acceleration techniques for ROS 2 computational graphs using Isaac ROS and AI Agent integration to speed up robotics workloads.

Multibody/Robotics

PIVOT combines physically-informed reasoning with vision-language models for off-road traversability assessment, enhancing navigation safety in unstructured terrain for field robots.

Multibody/Robotics

This paper addresses kinematics estimation and closed-loop control for tensegrity-based continuum robots by parameterizing complex continuous bending configurations using strut inclination angles, overcoming limitations of elastic backbone models.

Multibody

Proposes AffordanceWAM, an affordance-aware generative World Action Model that leverages both action-labeled robot videos and egocentric human videos for generalizable robot manipulation.

Multibody

Proposes bilinear Koopman model-based diffusion (BK-MBD) to reduce computational cost in real-time robot control by decoupling state lifting from candidate propagation.

Multibody

ForceTwin learns joint physical properties including inertia, friction, and spring mechanisms from human interaction data to build physics-informed digital twins that predict robotic manipulation actions.

Multibody

This paper investigates physical diagnosis for robots facing unfamiliar conditions, defining six ordered empirical conditions required for behavior improvement, and validates the chain in controlled and public environments.

ScientificML

NeuIDO proposes a neural intrinsic dynamics operator that internalizes physical constraints into world models for predicting 3D object interaction trajectories, bridging the gap between physics-informed 4D generation and true world models.

Multibody/Robotics

Introduces a computational framework for mechanical systems combining reservoir encoding and active inference, enabling adaptive learning in changing environments beyond traditional passive design and offline optimization constraints.

Multibody

LOInK learns a bi-Lipschitz invertible mapping from configuration space to a decoupled task/latent space, structuring the latent space so that cost-minimizing solutions are placed at the origin, enabling efficient sampling of approximately optimal inverse kinematics solutions.

Multibody/Robotics

Proposes D3DWA, an adaptive DWA framework using Dueling Double Deep Q-Network to adjust weights and prediction horizon for improved robot navigation in varied environments.

Multibody

DeViGrasp introduces methods to systematically examine how occlusion, segmentation-mask dropout, depth noise, and target-localization jitter affect grasp reasoning and target tracking for quadruped manipulators, improving whole-body control robustness under degraded perception.

Multibody

CHOREO enables training-free composition of heterogeneous humanoid skills learned via reinforcement learning, motion imitation, and generative modeling, bridging incompatible representations, interfaces, and controllers through unified trajectories.