🔥 [ICLR 2026] Benchmark for robotic tabletop manipulation memory-intensive tasks
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Updated
Jun 30, 2026 - Python
🔥 [ICLR 2026] Benchmark for robotic tabletop manipulation memory-intensive tasks
Squint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics [PyTorch, SO-101 Robot Arm, ManiSkill3, Sim-to-Real]
🔥[AAMAS 2026 Oral] Don’t Blind Your VLA: Aligning Visual Representations for OOD Generalization. https://blind-vla-paper.github.io
Official implementation of "Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories" (ENAP, RSS 2026): neuro-symbolic imitation learning that learns task structure from demonstrations as a state machine.
A minimal Vision-Language-Action model you can read: frozen CLIP + a tiny head on ManiSkill PickCube. LeRobot integration. Runs on a Mac, no GPU.
APPL is an agentic robot learning framework connecting policy learning with agent-based skill selection and composition through structural priors for generalization from a few demonstrations to new scenes and task compositions.
Minimum example showing how to setup Singularity with Maniskill3 to run training headless on a cluster.
Asset files for the Trossen Robotics WidowX AI robot for the ManiSkill framework
Benchmark for adaptation to hidden robot action-interface contracts: 4 ManiSkill tasks, preregistered gates, belief/probe/learned methods, delay-aware control. Same policy, different wiring — can it adapt?
RAIN: Refine Actions If Needed — adaptive action chunk refinement for robot manipulation (arXiv:2610.19765).
PPO reinforcement learning on the Unitree G1 humanoid — learning bipedal balance in MuJoCo and dexterous apple-in-bowl manipulation in ManiSkill 3, with side-by-side training-progression demos.
Self-hosted robotics workflow workbench: simulation integrations, dataset tools, training and artifact provenance. Alpha; trusted-local use.
Training and deploying ACT policies for SO-101 cube grasping: motion-planning data collection, ManiSkill-to-LeRobot conversion, then simulation and real-robot evaluation.
Reproducible ManiSkill PickCube visual imitation-learning workflow for MLP BC and ACT.
Tight-clearance insertion demonstrations in ManiSkill: 4cm cube into a 4.2cm tray cell, and a phone into a slot via two-Panda handoff. Collected for PI0.5/VLA training.
双相机 Qwen3.5 VLA:可信数据、Rectified Flow Action Chunk、ManiSkill 闭环评估与 temporal ensemble
Reproducible comparisons of open robotics AI models, data pipelines, simulators, and contribution workflows
Predicted Futures Are Not Enough: Learning Executable Goals for Robot Manipulation — code, frozen goal banks and evaluation records
Reproducing PPO on ManiSkill's PushCube task, with reward shaping and curriculum learning experiments.
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