Tag

Guava: An Effective and Universal Harness for Embodied Manipulation

Provides a harness that lets language models control embodied manipulation via iterative perception–reasoning–action loops, semantic action abstractions, and multimodal observations. Demonstrates distilling capabilities into a 4B open-source model with under 2K simulated trajectories and shows sim-to-real generalization.

WGO-Bench

Provides a small, manually annotated benchmark for evaluating vision–language models that convert robot and egocentric manipulation videos into timestamped subtask segments and concise action labels. Contains 100 episodes, 743 gold segments, and MP4 bytes embedded per row.

Domain Arithmetic: One-Shot VLA Adaptation under Environmental Shifts

Adapts pretrained Vision-Language-Action (VLA) models to new camera poses and robot embodiments from a single demonstration by performing weight-vector arithmetic that injects domain-specific information. Filters noise via subspace alignment of singular components; designed for one-shot adaptation under visual and embodiment shifts.

Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots

Provides a portable C++ inference runtime to deploy embodied AI models (vision–language–action and world–action) on heterogeneous robot hardware, enabling latency-first batch-1 closed-loop control. Key features include modular multi-rate layers, fused low-latency inference, and extensible head/IO plugins.

GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation

Provides a systematic benchmark and design roadmap for video-based world models to evaluate robot policies, introducing WMBench and GigaWorld-1 optimized for long-horizon, action-faithful rollouts. Offers controlled comparisons across model families, action encodings, and 324k+ simulated vs real rollouts, with code, models, and datasets released for reproducible evaluation.

VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon

Detects when an action-chunked VLA policy drifts from expected visual dynamics and triggers lightweight corrective replanning via a latent-space vision monitor and online gradient guidance; creates an event-driven adaptive action horizon without retraining the backbone.

RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies

Evaluates generalist robot manipulation policies across simulation and real-world settings using 42 sim tasks and 18 real tasks; measures generalization, memory, precision, long-horizon execution and open-vocabulary instruction following, and provides a cloud-accessible real-world evaluation system with XPolicyLab integration and a public leaderboard.

Open-AoE — Egocentric Hand Manipulation Dataset

Provides structured egocentric manipulation signals from smartphone videos: MANO 3D hand reconstructions, metric camera trajectories, and fine-grained atomic action segments (full release ≈2,000 hours planned). Supplies aligned hands.npz, camera_traj.npz, undistorted intrinsics and segment annotations for embodied-learning pipelines.

ClothTransformer Dataset

Provides 2,056 penetration-free cloth simulation trajectories (240 frames each, 493,440 frames, ~33 GB) across human garments, robotic manipulation, and object-collision scenarios. Includes per-vertex positions, per-frame displacements, mesh topology and collision fields under CC BY 4.0 — useful for training and evaluating learning-based cloth simulators.

Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence

Pretrains a DiT-based Mixture-of-Experts video foundation model for embodied intelligence by augmenting internet videos with robot-centric footage and using a multi-dimensional reward system to prioritize physical realism and task completion while scaling MoE for better capacity vs. inference trade-offs.

Dual Latent Memory in Vision-Language-Action Models for Robotic Manipulation

Reconstructs historical experience into latent memory tokens and weaves short- and long-term latent memories directly into vision-language-action reasoning to improve long-horizon robotic manipulation. Uses a four-part pipeline (curator, seeker, condenser, weaver) so memory participates natively in multimodal action formation.

ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory

Provides a deliberative Agent OS layer for robots that handles scene-conditioned planning, context-isolated skill execution, multi-stage verification, persistent multi-modal graph memory, and edge–cloud collaboration. Introduces EmbodiedWorldBench (16 scenes, 200+ tasks) and a failure-driven self-evolution loop; shows improved task success and strong memory benchmark scores.