864 papers
This paper extends the existing Responsibility metric for Social Robot Navigation by introducing time normalization for conflict buildup and a new Engagement metric to measure conflict intensification, demonstrating through simulated scenarios that these tools effectively evaluate the quality and foresightedness of cooperative conflict resolution in dyadic, group, and crowd interactions.
This paper introduces Diffusion-Based Impedance Learning, a framework that combines a Transformer-based diffusion model with energy-consistent impedance control to enable robots to learn and adapt contact-rich manipulation behaviors from teleoperated demonstrations, achieving high-precision performance and robust generalization in tasks like peg-in-hole insertion.
The paper presents GUIDE, a novel autonomous exploration framework that integrates global graph inference with a diffusion-based policy to effectively model unobserved spaces and generate efficient paths, achieving significantly faster coverage and reduced redundancy compared to state-of-the-art methods.
This paper proposes a reliable, computation-efficient system for 3D localisation of distant objects in safety-critical surveillance tasks like wildfire monitoring by applying multi-view triangulation or particle filters to noisy image segmentation sequences from drones, offering shape and uncertainty estimates without requiring specialised sensors or full 3D reconstruction.
This paper presents a novel approach for robust sim-to-real in-hand manipulation of articulated tools by combining a simulation-trained base policy with a sensor-driven refinement module that uses cross-attention to fuse tactile and force-torque feedback, enabling online adaptation to real-world joint phenomena and perturbations across diverse tools without precise physical modeling.
This paper proposes a peer-to-peer robot collaboration framework where robots use Large Language Models and Signal Temporal Logic to autonomously negotiate help requests and offers, enabling them to resolve conflicts and minimize system makespan without relying on a central task allocator.
This paper introduces EgoTraj-Bench, the first real-world benchmark for ego-centric trajectory prediction under noisy observations, and proposes BiFlow, a dual-stream flow matching model with an EgoAnchor mechanism that achieves state-of-the-art robustness by concurrently denoising historical inputs and forecasting future motion.
This paper proposes a Conflict-Based Search (CBS) protocol that enables efficient, collision-free multi-agent motion planning for heterogeneous teams of robots with independent tasks by utilizing a central planner that coordinates diverse single-agent solvers—ranging from traditional algorithms to learning-based methods—through a standardized space-time constraint API.
The paper introduces Seeing the Bigger Picture (SBP), an end-to-end mobile manipulation framework that utilizes a 3D latent map to aggregate long-horizon observations and global context, thereby outperforming image-based policies in spatial reasoning and task success rates across both known and novel scenes.
MachaGrasp is an eigengrasp-based, end-to-end framework that generates dexterous grasp articulations across different hand embodiments by leveraging morphology embeddings and a kinematic-aware loss, achieving high success rates in both simulation and real-world few-shot adaptation scenarios.
This paper introduces CBF-RL, a framework that integrates Control Barrier Functions directly into the reinforcement learning training process to internalize safety constraints, thereby enabling safe, robust, and filter-free deployment of policies in real-world scenarios like humanoid robot navigation.
This paper introduces Least Restrictive Hyperplane Control Barrier Functions (H-CBFs), which optimize the orientation of a separating hyperplane to reduce the conservativeness of traditional distance-based safety guarantees, thereby enabling safer and less restrictive control for dynamic systems navigating complex obstacles.
This paper proposes a hybrid state tree-based kinodynamic Task and Motion Planning (TAMP) framework that leverages a Vision-Language Model (VLM) to guide exploration and backtrack based on visual rendering, significantly improving success rates and planning efficiency in complex long-horizon tasks compared to traditional and LLM-only approaches.
This paper introduces SpikeATac, a multimodal tactile finger combining high-speed dynamic PVDF sensing with static capacitive sensing and a reinforcement learning framework to enable the dexterous in-hand manipulation of fragile objects.
This paper presents a new class of coning compensation algorithms for strapdown navigation systems that are derived directly from classical Runge-Kutta integration routines using Lie theory, offering a systematic method to generate higher-order corrections that encompass existing popular algorithms.
This paper introduces ROVER, a regulator-driven framework for verifying and iteratively improving black-box robot policies against prioritized Signal Temporal Logic temporal safety requirements using robustness metrics, which has been shown to significantly increase specification satisfaction rates in both simulated and real-world navigation scenarios.
The paper introduces Observer-Actor (ObAct), a novel active vision imitation learning framework for dual-arm robots that dynamically assigns one arm to construct a 3D Gaussian Splatting representation and identify optimal viewing angles for the other arm, thereby significantly enhancing policy robustness and performance by reducing occlusions compared to static-camera setups.
MarketGen is a scalable simulation platform featuring an agent-based procedural content generation framework that automatically creates realistic, multi-modal supermarket environments and establishes a new benchmark for evaluating embodied agents on complex long-horizon tasks like checkout unloading and in-aisle item collection.
The paper proposes LAP, a fast latent diffusion planner for autonomous driving that operates in a VAE-learned latent space to disentangle high-level semantics from low-level kinematics, enabling single-step inference with state-of-the-art closed-loop performance and up to a 10x speedup over existing methods on the nuPlan benchmark.
The paper proposes GRAND, a hybrid hierarchical algorithm that combines reinforcement learning-based graph guidance with minimum-cost flow rebalancing and local assignment to achieve up to 10% higher throughput than state-of-the-art schedulers for large-scale, lifelong multi-agent pickup-and-delivery tasks while maintaining real-time execution.