AI Agent Papers2022
Grounds language-based robot planning in learned skill value functions, selecting actions that are both useful and feasible. PaLM-SayCan reports 84% planning and 74% execution success across 101 real-world instructions.
AI Agent Papers2022
Turns language commands into executable robot policies that combine perception, control APIs, loops, and geometry libraries. Hierarchical code generation recursively fills in missing functions to support new behaviors without policy retraining.
Embodied AI2024
Builds a roughly 42 cm bipedal robot designed to cost under $400 in parts. Its locomotion policies are trained in MuJoCo and tested on the physical robot, making sim-to-real transfer the central challenge.
Embodied AI2025
Trains PPO motion policies for the Microduck biped in MuJoCo Warp and exports them to ONNX for real-robot deployment. Its sim-to-real approach models small-servo behavior and gear backlash rather than relying on idealized actuators.
AI Dataset2026
Provides SimReady 3D assets for robotics and industrial simulation. The OpenUSD content is prepared for simulation workflows, helping teams assemble environments for physical AI.
Embodied AI2026
Autonomously generates, iteratively debugs, and composes robot control programs into a reusable skill library for discovering and refining manipulation skills across simulation and real robots. Uses a code-as-policy continual learning loop—accumulating validated sensorimotor skills instead of model weights—to tackle long-horizon household and benchmark tasks.
AI Dataset2026
Provides 1,800+ hours of synchronized egocentric multi-view recordings with 3D hand reconstructions, wide‑FOV depth, and hierarchical task/subtask annotations for embodied AI and robot learning. Includes six fisheye views, hand meshes, and per-episode temporal labels across 44k+ episodes.
AI Dataset2026
Generates articulated motion for arbitrary rigged 3D skeletons from text prompts with a single unified model and no per-skeleton retraining. Uses topology-aware diffusion-transformer architecture and a large UniML3D motion dataset to generalize across bipeds, quadrupeds, and other topologies; suitable for text-to-animation workflows and rapid prototyping.
Embodied AI2026
Runs reinforcement-learning policies on a tiny biped robot, enabling behaviors such as walking, self-righting, and picking up objects. Policies are trained in simulation and run locally on the robot.
AI Model2026
Integrates a pretrained vision–language model with a BEV perception head and a Planning Expert to provide 3D perception, driving VQA and motion planning for autonomous driving while keeping the base VLM architecture unchanged.
Embodied AI2026
Wei Wang, Wenqiao Zhang +15Affiliation: College of Computer Science and Technology, Zhejiang University, Affiliation: Nanjing University of Aeronautics and Astronautics +4
Wraps vision–language–action policies into executable skills that are runtime-validated, executed as bounded low-level action chunks, outcome-verified, and logged as structured trajectories. A fixed skill interface enables swapping or adapting low-level VLA policies and provides component-level supervision for training and optional online adaptation.
Trains a world-action model that predicts future visual states to guide zero-shot robotic manipulation; introduces CoAE, SVP, IDM and KASO to pretrain generative and action components from scratch on manipulation data, scaling up to 30,000 hours and improving zero-shot success.