All papers

Presents an external revisable memory layer that tracks and retracts dynamic manipulation conditions for frozen vision-language-action policies.

Develops a simulation platform and policy framework for robot navigation combining visual observations with overheard ambient Wi-Fi signals.

Evaluates geometric verifiers that coordinate multi-robot path planning by translating trajectory conflicts into structured feedback for language models.

Presents a three-stage learning framework for whole-body reactive humanoid control from egocentric visual inputs to catch flying objects.

Combines iterative covariance steering and adaptive control to provide safe proximity navigation for spacecraft around small celestial bodies.

Presents an integrated software architecture combining stereovision, augmented reality, and tracking modules for real-time surgical robotic navigation.

Develops an online data-driven predictive control method using tactile feedback for secure and gentle robotic grasping of unfamiliar objects.

Introduces a two-agent memory architecture with an auditing mechanism to ground long-horizon episodic context into symbolic subgoals for robot policies.

Presents an automated solver for cubic spline interpolation to generate smooth, continuous waypoint trajectories for autonomous motion systems.

Proposes an action-space grounding method that projects tool affordances to transform pretrained vision-language models into robotic manipulation policies.

Proposes a counterfactual training method that mitigates head-task action bias in robot policies by isolating instruction- and action-relevant visual evidence.

Develops a temporal soft mixture-of-experts teacher-student distillation framework for versatile robot navigation across dynamic environments.

Introduces a token-allocation scheme combining sparse future references and dense execution targets within world action models.

Combines kinodynamic sampling-based motion planning with latent visual world models to solve long-horizon manipulation tasks.

Presents a lightweight vision-language-action policy trained with optical-flow future motion prediction to guide robot manipulation.

Proposes an adaptive whole-body control framework combining model predictive control, posture planning, and balance compensation for humanoids lifting unknown payloads.

Presents an imitation learning framework that jointly plans camera viewpoint repositioning and manipulation actions under partial object occlusions.

Presents a framework that recasts offline navigation demonstrations across different robot embodiments without collecting new target-embodiment data.

Proposes a guided dataset design method based on map data to develop urban perception benchmarks including heavy-duty transport modalities.

Presents a retargeting pipeline that extracts contact-aware motions from monocular human videos to teach loco-manipulation skills to humanoid robots.

Presents a replanning framework that scales trajectory revision to belief agreement for humanoid robots manipulating unfamiliar objects.

Introduces a residual action tokenization method that uses decoded reconstruction feedback to refine discrete action tokens for autoregressive robot policies.

Introduces robotic action models that supervise policy representations via action-conditioned future-state prediction while avoiding future-generation inference costs.

Presents a wrist-centric demonstration framework that transfers human hand manipulation data to multiple distinct robotic hands without embodiment-specific demonstrations.

Enables low-latency reactive micro-aerial vehicle collision avoidance by computing Euclidean Signed Distance Field queries directly on posed depth images.

Generates physically grounded synthetic manipulation demonstrations by combining video diffusion world models with physics-engine trajectory alignment.

Accelerates constrained humanoid motion planning on GPUs by exploring tangent spaces rather than sampling ambient configuration manifolds.

Improves real-world robot reinforcement learning through selective experience reuse filtered by positive advantage estimates and human interventions.

Generates text-conditioned humanoid motions using a part-aware latent diffusion model that incorporates kinematic body structure.

Enables recursive self-improvement of endoscopic robot policies using an LLM coding agent guided by closed-loop physical experimental feedback.

Detects manipulation failures from nominal demonstrations using object-centric spatio-temporal graphs and geometric correlation scoring.

Generates and refines diverse autonomous driving trajectory hypotheses in one step using mixture-of-experts flow matching and world models.

Distills reflective diagnostic guidance into single-pass autonomous driving vision-language-action models using dual-path on-policy self-distillation.

Learns reactive humanoid tennis policies by matching generic locomotion data with internet tennis videos through trajectory-guided motion distillation.

Introduces a simulation benchmark evaluating robotic dexterous manipulation in wet-lab environments under dynamic visual phenomena like vapor and flames.

Proposes a unified taxonomy classifying agricultural harvesting technologies across physical interaction mechanisms and levels of automation.

Detects LiDAR degradation in degraded visual environments using an LSTM autoencoder to adaptively transfer odometry weight to radar sensors.

Proposes a mapless, drift-free flipper controller for tracked robots using short-window motion cues and IMU stabilization.

Generates anticipatory 3D robot navigation representations from single RGB images using text-prompted video diffusion and geometric depth alignment.

Combines language-model task planning with onboard behavior-tree skill execution and multi-fidelity benchmarking for quadruped robot deployments.

Adapts pretrained vision-language-action models by learning context-conditioned noise distributions while freezing the underlying base policy.

Proposes distributed harmonic-covariance feedback using Hodge Laplacians for hole closure in planar multi-agent robot swarms.

Formulates a robust trajectory and timing planner for coordinated UAV-UGV aerial monitoring teams under energy and precedence constraints.

Proposes an algorithm for multi-robot submodular maximization under communication k-connectivity constraints resilient to robot failures.

Investigates conditions under which communication signals emerge between simulated autonomous robots performing spatial foraging tasks.

Develops trajectory generation and geometric control for dual-quadrotor cooperative transport of payloads with sliding cable attachments.

Introduces a configuration-driven platform for reproducible multi-agent reinforcement learning experiments within the ARGoS swarm robotics simulator.

Develops a low-cost piezoresistive glove system that aligns human touch demonstrations with robot sensors via contact events.

Proposes a reliability-aware conditioning method that mitigates harmful temporal misalignments when using generated video futures for robot manipulation.

Introduces an adaptation framework for frozen vision-language-action policies using residual reinforcement learning and online capability inference during joint faults.

This paper presents a feasibility-aware safety filter that amortizes minimal-intervention constraints using a feedforward neural network in reinforcement learning.

This paper trains a generalist humanoid policy by predicting body and hand motion latents mapped from human demonstration data.

This paper analyzes data sampling distributions across parallel simulations to improve exploration efficiency in reinforcement learning for robot control.

This paper introduces a training-free safety filter that evaluates scene context to constrain generated humanoid motion trajectories.

Presents fixed-point flow matching, a data-free procedure that trains flow policies in reinforcement learning without requiring demonstration data.

Develops a motion-prediction hierarchical control framework to plan safe touchdown windows for autonomous drone landings on surface vessels.

Uses a convolutional LSTM on satellite data to evaluate the predictability and observational limits of year-to-year land degradation dynamics.

Evaluates Transformer models trained with quantile loss on historical vessel position data to produce probabilistic ship trajectory forecasts.

Introduces a replay benchmark to evaluate robot motion smoothness and controller latency in vision-language-action manipulation pipelines.

Presents a compressed latent world-action model enabling faster robotic manipulation inference on resource-constrained edge hardware.