667 papers
This paper presents a physics-informed model derived from hexapedal robot experiments on granular slopes that identifies delayed anchoring and backward slip as primary failure mechanisms, enabling the construction of phase diagrams for quantitative risk estimation of locomotion on deformable inclines.
This paper proposes a unified two-stage framework that restores feasibility for conflicting Signal Temporal Logic (STL) specifications by first applying minimal relaxation and then refining the solution through Pareto-optimal multi-objective optimization, thereby enabling interpretable decision-making and avoiding deadlocks in safety-critical applications like autonomous driving.
This tutorial critically examines the gap between theoretical Control Barrier Function (CBF) guarantees and practical implementation in robotics, revealing how common misuses and hidden assumptions often lead to tautological safety claims in passively safe systems while offering guidelines and interactive tools to construct valid safety arguments for systems with input constraints.
This paper proposes a rotating equilibrium strategy for collaborative tethered aerial transport, where steady circular motion generates centrifugal forces to maintain tether tension, thereby enabling quadrotors to produce purely vertical thrust and reducing total power consumption by up to 20% compared to conventional static lifting methods.
This paper introduces VSL-Skin, a novel voxel-based phase-change system that bridges soft and rigid robotics by enabling individually addressable, centimeter-scale stiffness modulation, 30% axial compression, and autonomous self-repair to create programmable virtual joints and variable-stiffness morphologies.
This paper introduces a lightweight, probabilistic world model built on a pretrained vision foundation model that generates uncertainty-based runtime monitors to accurately detect anomalous failures in bimanual manipulators, outperforming existing baselines while requiring significantly fewer trainable parameters.
This paper proposes a robust two-stage framework for mobile manipulator path planning that combines dimensionality-reduced graph search with numerical optimization to efficiently generate smooth, kinematically feasible trajectories with sub-millimeter accuracy.
This paper introduces the Safety-guaranteed Surgical Policy (SSP) framework, which integrates Neural ODE-based uncertainty modeling with robust Control Barrier Functions to enforce behavioral and spatial constraints, thereby ensuring near-zero safety violations while maintaining high task success rates in data-driven robot-assisted surgery.
TacDexGrasp is a robust dexterous grasping framework that utilizes tactile feedback and a Second-Order Cone Programming controller to actively constrain tangential-to-normal force ratios, thereby preventing both translational and rotational slip without requiring explicit torque modeling or slip detection.
This paper introduces GuideTWSI, a diverse dataset combining synthetic and real-world images to address the scarcity of Tactile Walking Surface Indicator (TWSI) data, specifically bridging the gap between East Asian directional bars and North American/European truncated domes to improve navigation safety for blind and low-vision individuals.
This paper proposes a morphology-independent facial expression imitation method that decouples expression semantics from facial morphology using self-supervised learning and an error-perceiving transfer module, validated on a custom-designed human-face robot named Pengrui to achieve more natural and accurate human-robot interaction.
The paper introduces Artoo, a lightweight, end-to-end neural acoustic communication system for robots that co-trains a compact text-to-speech transmitter and ASR receiver to achieve distortion-robust, high-accuracy decoding in noisy environments without preserving human-like speech qualities.
VLN-Cache addresses the inference cost of Vision-and-Language Navigation models by introducing a training-free token caching framework that overcomes the limitations of static assumptions through view-aligned remapping for visual dynamics and a saliency filter for semantic dynamics, achieving up to a 1.52x speedup while maintaining navigation performance.
This paper proposes ACLM, an ADMM-based distributed model predictive control framework that enables scalable, real-time collaborative loco-manipulation of heavy payloads by decomposing the global optimization problem into parallel robot-level subproblems while preserving dynamic coupling through consensus constraints.
This paper proposes a scalable, structured multitask variational Gaussian Process framework for full-body human motion prediction that achieves competitive accuracy with significantly fewer parameters and provides well-calibrated, interpretable uncertainty estimates essential for safe real-time human-robot collaboration.
The paper proposes Failure Episodic Memory Alert (FEMA), a technique that stores and retrieves short-horizon failure experiences to prevent robots from relapsing into unstable states, thereby significantly improving sample efficiency and enabling successful long-horizon exploration in challenging contact-rich reinforcement learning tasks.
This paper proposes a data-driven initialization strategy for autonomous racing trajectory optimization that utilizes a neural network trained on Formula 1 telemetry to predict expert-like raceline offsets, thereby significantly accelerating solver convergence and reducing runtime compared to traditional geometric baselines while maintaining optimal lap times.
DexKnot is a generalizable visuomotor framework that combines keypoint affordances with diffusion policies to enable robots to reliably knot plastic bags across diverse, unseen instances by learning shape-agnostic representations from real-world manual deformations.
This paper proposes and experimentally validates a model-based method that links actuator expansion to surface temperatures to compensate for thermal drift in high-precision hexapod robots, achieving a reduction in thermally induced errors of over 80%.
This tutorial provides a control-oriented introduction to aided inertial navigation systems by utilizing a Lie-group formulation based on the extended Special Euclidean group SE(2,3) to establish a clear, geometric framework for fusing inertial and aiding measurements while explicitly leveraging invariance and symmetry principles.