Robotics
QDTraj: Enhancing Robotic Manipulation through Diverse Trajectory Primitive Generation
QDTraj utilizes Quality-Diversity algorithms with sparse reward exploration to generate a diverse set of high-performing trajectory primitives for robotic manipulation. This approach enables robots to learn multiple solutions for tasks, improving adaptability to real-world constraints and unexpected…
Dexterous Tendon-Driven Wrist Achieves 99% Handkerchief Unfolding via Hierarchical Control and Particle-Spring Modeling
Researchers developed a parallel anti-parallelogram tendon-driven wrist enabling 90-degree omnidirectional rotation with low inertia and decoupled roll-pitch sensing for spinning flexible objects like handkerchiefs. A high-low hierarchical control scheme paired with a particle-spring model abstracts…
Switch: Hierarchical Multi-Skill System for Agile Humanoid Locomotion
The "Switch" system addresses limitations in humanoid robot skill transitions by introducing a hierarchical multi-skill framework. This framework utilizes a Skill Graph (SG) for kinematically similar transitions, a deep reinforcement learning-trained whole-body tracking policy, and an online skill s…
Benchmarking Classical Coverage Path Planning on Irregular Hexagonal Grids
This paper introduces a reproducible benchmark for deterministic single-vehicle coverage path planning heuristics on irregular hexagonal grids, relevant for maritime applications. It addresses the lack of standardized comparisons for these methods, which are often evaluated on limited examples or in…
D-REX: Differentiable Real-to-Sim-to-Real Engine for Dexterous Grasping
D-REX introduces a differentiable real-to-sim-to-real engine leveraging Gaussian Splat representations for robotic systems. This engine aims to bridge the simulation-to-real-world gap by enabling object mass identification from visual observations and control signals, while simultaneously facilitati…
Robotic Peeling with Human Preference Alignment
This paper presents a two-stage learning framework for fine-grained robotic manipulation tasks with subjective success criteria, using knife-peeling as a case study. The approach combines force-aware imitation learning for robust initial policy generation with preference-based finetuning using a lea…
XL-VLA: A Cross-Embodiment Latent Space for Dexterous Robot Manipulation
XL-VLA introduces a novel vision-language-action framework that utilizes a unified, embodiment-invariant latent action space. This approach enables scalable cross-embodiment training and efficient data reuse for dexterous manipulation tasks, addressing the challenge of costly data collection for div…
Physics-Aligned Simulation for Deformable Object Manipulation
Robotic manipulation of deformable objects is data-intensive due to complex shape, contact, and topological changes. Traditional sim-to-real methods fail because simulators are ungrounded and lack accurate physics for these objects. SIM1, a physics-aligned data engine, addresses this by digitizing s…
AEROS: A Single-Agent Operating System for Embodied AI
AEROS presents a novel architectural paradigm for robotic systems, treating a robot as a single persistent intelligent agent. Its core innovation lies in the use of Embodied Capability Modules (ECMs) that encapsulate skills, models, and tools, enforced by a policy-separated runtime. This design enha…
RoSHI: A Hybrid Wearable for Robust Human Pose and Shape Estimation in Robotic Learning
RoSHI is a novel hybrid wearable system designed to capture rich, long-horizon human interaction data for scaling robot learning. It integrates low-cost sparse IMUs with Project Aria glasses to achieve precise 3D pose and body shape estimation in a global coordinate frame. This approach addresses li…
EgoVerse: Scaling Robot Learning Through Egocentric Human Data, Bypassing Teleoperation
EgoVerse leverages egocentric human data to scale robot learning, moving beyond traditional teleoperation. This approach, supported by the EgoScale and dexterity scaling law, uses behavior cloning from human actions to enhance robot capabilities without direct robot interaction during the learning p…
Robotics Software Lags Hardware, Hampered by Reliability and Misaligned AI
Robotics development is currently bottlenecked by hardware reliability issues, which slow down software iteration despite advanced physical capabilities. The field also suffers from a lack of standardized benchmarking, leading to irreproducible results and difficulty in objective comparison. Further…
Amazon FAR Open-Sources Holosoma: A Full-Stack Robotics Platform for Sim-to-Real Transfer
Amazon FAR has open-sourced Holosoma, a comprehensive robotics platform designed to address the full-stack challenges of sim-to-real learning for humanoid robots. Holosoma provides a unified framework supporting multiple simulation backends (IsaacGym, IsaacSim, MJWarp), various robots (humanoid and …
Robotics: Conquering the Physical World with AI
While AI has largely conquered the digital domain, the next grand challenge lies in mastering the physical world. This requires a data maximalist and model minimalist approach, leveraging synthetic data generated through advanced simulation and video world models. The ultimate goal is to achieve a "…
World-Model-Guided Trajectory Generation Unlocks One-Shot Robot Imitation on Unseen Tasks
OSVI-WM addresses a critical gap in one-shot visual imitation learning: generalizing to unseen tasks that are visually similar to training tasks but require semantically distinct responses. The framework uses a learned world model to predict latent state-action trajectories from a single expert vide…
RoboCulture Enables Cost-Effective Robotic Automation of Long-Duration Biological Experiments
RoboCulture is a flexible, low-cost platform using a general-purpose robotic manipulator to automate biological workflows, addressing limitations of current liquid handlers that require human intervention for plate loading, tip replacement, and calibration. It integrates liquid handling, lab equipme…