All papers

The paper adds a lightweight decision head to a robot policy to call an external reasoning model only during critical recovery events.

Introduces GPU-parallel adaptive mesh refinement to simulate deformable object manipulation efficiently by focusing resolution on contact areas.

Presents a method to forecast surgical tissue deformation from preplanned instrument trajectories using observation-referenced dynamic models.

Proposes an offline smoothing method to estimate and map tire-road friction coefficients from standard on-board vehicle sensors.

Presents a dynamics compiler extension that models coupled multi-body kinematics and forces for teams of physically connected humanoids.

Introduces an authorization and monitoring framework to verify and bound autonomous excavator motions generated by language models.

Guides long-horizon world-model planning in robotic manipulation using video-generated visual subgoals and learned policy priors.

Combines vision-language models with spatial overlays and chain-of-thought prompting to plan coordinated paths for multi-robot teams.

Presents a residual learning approach for physics-based demonstration retargeting to achieve zero-shot robotic grasping across arbitrary objects.

Introduces quality-diversity optimization to generate adversarial environments for red-teaming and fine-tuning vision-language-action models.

Demonstrates a shared-control teleoperation system enabling continuous industrial valve rotation using omnidirectional aerial robots.

Proposes a two-stage polar gradient field method for real-time vision-based aerial vehicle trajectory planning in dynamic environments.

Develops a fourth-order control barrier function framework incorporating full nonlinear dynamics for safe quadrotor obstacle avoidance.

Presents a decentralized multi-robot manipulation method that predicts teammate responses to coordinate collective actions under local sensing.

Introduces a multi-stage reinforcement learning framework to teach humanoid robots generalizable object carrying directly from video demonstrations.

Proposes an action-filtering framework enabling robots to infer human visual visibility and adapt behavior during human-robot interactions.

Introduces a seven-dimensional deformation model based on similarity transformation groups to simulate expanding and contracting soft robots.

Introduces a learning framework enabling quadruped robots to estimate carried object geometries from contact and adapt navigation paths.

Develops an embodied agent architecture using finite-state machine skill representations to structure and reuse multimodal interaction policies.

Proposes a sequential testing method to reliably rank robotic policies with minimal physical hardware trials.

Presents an asynchronous cloud-edge dual-policy architecture connecting high-level visual guidance to fast local trajectory planning for navigation.

Provides theoretical conditions and explicit noise bounds guaranteeing the global optimality of pose graph optimization in robotics.

Introduces a benchmark evaluating collision safety and blocking dynamics in vision-and-language navigation agents.

Introduces a training-free framework for multi-altitude drone collaboration in search-and-rescue using belief-guided memory coupling.

Proposes a training method for vision-language-action policies to fix supervision imbalance and improve high-precision robotic manipulation.

Presents a co-simulation framework to evaluate heterogeneous computing systems and closed-loop flight performance for drones before building hardware.

The paper introduces CAG-WAM, applying a structural causal model to isolate action-induced improvements in world action models for manipulation.

The paper details B-Human's behavior architecture and system modifications for scaling humanoid soccer play up to 11-versus-11 matches.

The paper introduces TempoLoco, a body-aware dynamic visual perception framework that adapts visual memory to current quadruped locomotion states.

The paper evaluates whether rapid motor adaptation conditioning on latent dynamics scales effectively to coordinated bimanual robotic manipulation tasks.

The paper presents a learning-based framework using multi-head attention to enable high-speed sprinting and aggressive cornering on full-scale humanoid robots.

The paper proposes a conditional flow matching method to synthesize dynamic 4D hand-object grasping sequences from single initial configurations.

The paper presents DEFT-VLA, a dual-expert vision-language-action framework specializing force feedback specifically for contact stages during robotic manipulation.

The paper proposes a contextual masked action policy that performs history-conditioned local span regeneration to refine continuous robotic action chunks.

The paper presents Verify, Act, Distill, distilling inference-time search rollouts into LoRA parameters for vision-language-action models without reinforcement learning.

The paper proposes LETS, a physics-guided residual learning method for real-time external-torque sensing in force-feedback bilateral teleoperation.

The paper introduces test-time command optimization to reduce trajectory errors in humanoid behavior foundation models via closed-loop simulation search.

The paper develops funnel motion planning and control for robotic manipulators using neural signed configuration distance functions for collision avoidance.

The paper proposes an edge-native dual-expert model using actuator-load and joint-angle feedback for multi-finger grasp classification and stop prediction.

The paper presents FoundationVIO, integrating inertial sensing with feed-forward geometric foundation models to recover metric scale in visual odometry.

The paper introduces EnactOpt, an optimization method that evolves execution harnesses using diagnostic feedback to improve embodied agent performance.

The paper introduces FreeBar, a training-free barrier-constrained inference plugin for real-time asynchronous execution of vision-language-action models.

The paper evaluates a shared-visualization teleoperation interface to improve operator coordination and situational awareness in multi-robot systems.

The paper presents a training-free graph optimization framework that coordinates multi-agent embodied search using uncertain vision-language model priors.

The paper proposes inverse kinematics and compliance optimization control for a rigid-soft hybrid-link humanoid robot modeled using piece-wise constant strain.

The paper uses mutation testing to evaluate whether fuzzy logic-based risk estimation improves situation coverage for autonomous mine-exploring drones.

The paper presents nanoflann, a C++ KD-tree library supporting exact nearest-neighbor search on user-defined product manifolds at compile time.

The paper introduces a decentralized reinforcement learning policy for heterogeneous maritime multi-robot task allocation under spatio-temporal environmental uncertainties.

The paper proposes an inference-time search method to select embodiment-feasible humanoid reactions from human-to-humanoid retargeted motion models.

The paper presents ProWorld, a multi-view world model with proprioceptive feedback that tracks robot policy success rates in closed-loop evaluation.

The paper presents a memory-augmented world action model combining visual state and latent event memory for long-horizon robotic manipulation.

The paper introduces an open-source MuJoCo simulation environment for aerial robots equipped with tendon-driven soft continuum arms.

The paper reviews how robotic physical models can help investigate whole-body mechanical interactions and sensing across complex physical terrains.

The paper proposes a hierarchical navigation framework that coordinates exploration, error recovery, and revisiting past locations for robotic object search.

The paper introduces an uncertainty-guided spatial mapping framework to improve target search efficiency in zero-shot object navigation.

The paper investigates multi-agent reinforcement learning algorithms to balance human guidance and geometric safety in robotic surgical shared control.

The paper evaluates combinatorial skill generalization in embodied manipulation and provides a modular diagnostic benchmark for chained robot tasks.

The paper proposes a constrained reinforcement learning framework for whole-body loco-manipulation by quadruped robots operating in simulated lunar gravity conditions.

The paper introduces a constrained reinforcement learning method for end-to-end approach, capture, and detumbling of non-cooperative orbital targets by a spacecraft.

The paper presents a reinforcement learning approach for quadruped locomotion directly from raw lidar point clouds without building elevation maps.