695 papers
This paper introduces DEFT, a diffusion-based trajectory generator that enables robots to achieve fail-active operation by successfully completing tasks under arbitrary actuation failures, outperforming classical methods in both simulation and real-world scenarios while demonstrating robust zero-shot generalization.
This paper empirically analyzes robotics literature from 1980 to 2024 to reveal the lack of clear definitions and examples for "dull, dirty, and dangerous" (DDD) work, then synthesizes social science insights to propose a framework that guides the robotics community in better conceptualizing and addressing the impact of their technology on human labor.
OmniGuide is a universal framework that enhances the performance of generalist Vision-Language-Action (VLA) policies on complex tasks by integrating diverse guidance sources—such as 3D foundation models and human pose estimators—into a unified differentiable energy field that guides action sampling through task-specific attractors and repellers.
This paper presents a model-free, data-driven computational pipeline that jointly optimizes the layout of flexible sensors and the parameters of a shape prediction network to enhance deformation proprioception accuracy while ensuring manufacturability, eliminating the need for physical simulation models.
This paper presents the first systematic evaluation of uncertainty in vision-language model-based early action anticipation for human-robot interaction, introducing a temporal-prefix protocol and metrics to characterize miscalibration and ensure reliable, confidence-gated decision-making under partial and ambiguous observations.
The paper proposes AR-VLA, a standalone autoregressive Action Expert that maintains long-lived memory to generate continuous, context-aware action sequences, effectively addressing the frequency mismatch between fast control and slow reasoning while outperforming traditional reactive Vision-Language-Action models in trajectory smoothness and task success.
The paper introduces XL-VLA, a vision-language-action framework that utilizes a unified, embodiment-invariant latent action space to enable scalable cross-embodiment training and improve dexterous manipulation performance across diverse robotic hands.
This paper introduces "Dance2Hesitate," an open-source multi-modal dataset comprising synchronized kinesthetic robot teaching and dancer motion capture data across three hesitancy levels, designed to facilitate the study and benchmarking of understandable, context-aware hesitancy in human-robot collaboration.
This study leverages the OpenRobotRehab 1.0 dataset to analyze how interface design and task constraints influence neuromotor behavior in healthy and post-stroke users during 6D isometric gaming, demonstrating that pathological features are detectable in end-effector force data and that a novel hidden Markov model based on sEMG signals effectively classifies neuromotor dynamics where traditional synergy-based methods fail, thereby informing the design of adaptive rehabilitation robots.
This paper proposes an autonomous underwater vehicle search strategy that leverages one-shot detection of sparsely distributed target species alongside their denser environmental context features to guide adaptive planning, enabling the robot to locate up to 75% of the targets in half the time required by exhaustive coverage.
This paper introduces SoftGM, an octopus-inspired distributed control framework that leverages a Graph Neural Network-based attention policy within a Centralised Training Decentralised Execution paradigm to enable segmented soft robotic arms to robustly reach targets in complex, contact-rich environments through online obstacle discovery without relying on global geometry.
This paper proposes a real-time, deformation-aware framework that integrates robotic ultrasound with a lightweight USCORUNet network to dynamically update static intraoperative CBCT scans, thereby enabling continuous soft-tissue monitoring and navigation refinement without repeated radiation exposure.
This paper proposes a perceptive hierarchical-task model predictive control (HTMPC) framework that utilizes Bayesian inference to dynamically model environmental changes, enabling mobile robots to efficiently and reactively execute sequential manipulation tasks in unstructured, semi-static environments without relying on precomputed maps.
This paper proposes a Hierarchical-Task Model Predictive Control framework that leverages robot redundancy to efficiently execute sequential mobile manipulation tasks, demonstrating significant improvements in trajectory tracking, path efficiency, and execution speed compared to state-of-the-art methods in real-world experiments.
This paper presents a degeneracy-resilient Teach and Repeat navigation system using FMCW lidar that overcomes the limitations of traditional ICP-based methods in geometrically sparse environments by leveraging Doppler velocity for stable odometry and curvature-aware scan-to-map localization.
The paper introduces DICE-RL, a sample-efficient reinforcement learning framework that refines pretrained generative robot policies into high-performing experts by using distribution contraction to amplify successful behaviors from online feedback, enabling mastery of complex long-horizon manipulation tasks from pixel inputs in both simulation and real-world settings.
This paper presents the user-centered design and development of a robot-assisted chemical dialysis system, validated through usability studies, which aims to alleviate scientists' workload during tedious multi-day purification procedures and accelerate scientific discovery.
The paper introduces UF-OPS, an update-free on-policy steering method that utilizes verifier functions trained on rollout data to dynamically guide black-box diffusion policies toward higher-success actions, achieving a 49% average improvement in real-world task success rates without modifying the base model parameters.
This paper introduces ReST-RL, a hierarchical reinforcement learning framework that decouples humanoid locomotion from payload stabilization via a residual module, achieving robust, zero-shot sim-to-real object balancing on the Unitree G1 without compromising gait stability.
PC-Diffuser is a safety augmentation framework that integrates a certifiable, path-consistent capsule barrier function directly into the denoising loop of diffusion-based trajectory planners to ensure forward invariance and dynamically feasible motion while preserving the learned path geometry.