Dexora is the first open-source VLA system for dual-arm dual-hand high-DoF manipulation, trained on 100K simulated and 10K real teleoperated trajectories with a discriminator-weighted diffusion policy, achieving 66.7% dexterous success versus 51.7% for baselines.
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Aloha 2: An enhanced low-cost hardware for bimanual teleoperation
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BiCoord is a new benchmark for long-horizon tightly coordinated bimanual manipulation that includes quantitative metrics and shows existing policies like DP, RDT, Pi0 and OpenVLA-OFT struggle on such tasks.
TouchGuide improves contact-rich robot manipulation by steering diffusion or flow-matching visuomotor policies with tactile feasibility scores from a contrastively trained Contact Physical Model.
IOI decouples deterministic kinematics from stochastic physics in interactive world models by rendering forward-kinematics trajectories into multi-view projections that guide a video generator, achieving SOTA fidelity and OOD generalization on RoboTwin.
MotionWAM conditions a policy on intermediate features from a video world model to predict unified whole-body motion tokens, enabling real-time humanoid loco-manipulation that outperforms VLA baselines by over 30% on nine Unitree G1 tasks.
A retrieve-then-steer method stores successful robot actions in memory and uses them to steer a frozen VLA's flow-matching sampler for better test-time reliability without parameter updates.
GR00T N1 is a new open VLA foundation model for humanoid robots that outperforms imitation learning baselines in simulation and shows strong performance on real-world bimanual manipulation tasks.
π₀ is a vision-language-action flow model trained on diverse multi-platform robot data that supports zero-shot task performance, language instruction following, and efficient fine-tuning for dexterous tasks.
FOCA improves few-shot VLA adaptation by explicitly predicting future interaction embeddings and implicitly aligning to goal observations, yielding up to 26% gains on real robots with only 20 demonstrations.
GASE automates high-fidelity simulation scene reconstruction from multi-view panoramic videos via Gaussian splatting, object extraction, and inpainting, yielding robot policies with under 10% performance gap versus real-world training.
HumanEgo reports 92.5% average success on four real robot tasks using only 15-30 minutes of human video per task and zero robot data, with zero-shot transfer to new robots and cameras.
VLBiMan framework enables generalizable bimanual manipulation from single human demonstrations via vision-language anchored task decomposition and adaptation without retraining.
DexTeleop-0 adds a tactile-driven adaptation loop to bimanual dexterous teleoperation that estimates contact points and applies localized force-compliant corrections via operational-space Jacobian updates.
A pipeline reduces a robot arm's rigid-body parameters from 65 to 39 via symmetry, fits them with OLS+SDP+CLIE on hand-designed trajectories, selects a central model via PCA, and audits inertia positive-definiteness to yield a feasible, accurate dynamic model.
TinyVLA achieves faster inference and higher data efficiency than OpenVLA on robotic manipulation tasks by initializing from high-speed multimodal models and adding a diffusion policy decoder, without any pre-training phase.
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