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ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation
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Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robustness, and the ease of teleoperation. We introduce ALOHA 2, an enhanced version of ALOHA that has greater performance, ergonomics, and robustness compared to the original design. To accelerate research in large-scale bimanual manipulation, we open source all hardware designs of ALOHA 2 with a detailed tutorial, together with a MuJoCo model of ALOHA 2 with system identification. See the project website at aloha-2.github.io.
Forward citations
Cited by 15 Pith papers
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MuJoCo Playground
An open-source, MJX-based robot learning framework with integrated batch rendering that provides fast training and demonstrates sim-to-real transfer on six robot platforms.
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A bimanual ACT policy runs at 10 Hz on an 8 GB Jetson Orin Nano Super with roughly 90-95% task success, and the paper documents when quantization is necessary and which layers TensorRT refuses to quantize.
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OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
A policy-agnostic two-stage real-world RL method learns tactile residual corrections on frozen visual policies, lifting contact-rich task success from 5–40% to 85–100% in under 80 minutes.
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Constraint-Preserving Data Generation for Visuomotor Policy Learning
CP-Gen uses keypoint-trajectory constraints to turn a single expert demonstration into many geometry- and pose-varied robot demos, and policies trained on them transfer zero-shot to the real world.
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CollaBot: Vision-Language Guided Simultaneous Collaborative Manipulation
Vision-language guided multi-robot large-object manipulation, reported at 52 percent simulation success in the body text but advertised as 72 percent in the metadata abstract, with no baseline comparison.
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H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation
Pre-training a diffusion-transformer robot policy on 338K human hand-manipulation episodes, then fine-tuning with modular adapters, improves bimanual manipulation success across simulation and real robots.
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TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types
A type-guided teleoperation system that selects predefined dexterous hand poses with a language model outperforms retargeting-based teleoperation on nine real-world tasks and improves imitation learning success.
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T-Rex: Task-Adaptive Spatial Representation Extraction for Robotic Manipulation with Vision-Language Models
A zero-training framework that adaptively selects spatial representation extractors per object and per task stage improves real-world robot manipulation success and efficiency over fixed-representation baselines.
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Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data
A hybrid 3D Gaussian splatting plus explicit mesh representation, optimized end-to-end with differentiable rendering and physics, reconstructs objects and calibrates robot poses from imperfect real-world RGB trajectories.
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WorldEval: World Model as Real-World Robot Policies Evaluator
WorldEval conditions a video generation model on a policy's internal action embeddings (Policy2Vec) and shows generated-video success rates correlate with real-world robot success rates.
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Global-Local Interface for On-Demand Teleoperation
A Global-Local teleoperation interface that separates coarse positioning from fine manipulation lets operators complete precise tasks faster and with higher success than using either mode alone.
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DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove
DOGlove is a low-cost, open-source haptic glove for dexterous teleoperation that improves performance on contact-rich tasks and supplies demonstrations for imitation learning.
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MEVION: Low-Cost Open-Source Data Collection System for Powerful and High-Speed Dual-Arm Manipulation
MEVION is an open-source dual-arm teleoperation platform with 60 Nm joint torque, built from e-commerce parts for about $14,000 per four-arm system, enabling heavier, faster manipulation data collection.
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Re$^3$Sim: Generating High-Fidelity Simulation Data via 3D-Photorealistic Real-to-Sim for Robotic Manipulation
A reconstruction and neural-rendering pipeline converts real tabletop scenes into photorealistic robot simulations, and policies trained only on simulated data transfer zero-shot to the real robot with an average succ...
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Spatial RoboGrasp: Generalized Robotic Grasping Control Policy
Spatial RoboGrasp combines AugFusion, monocular depth, and grasp prompts in a diffusion policy, claiming large gains under exposure change, without released artifacts or error bars.
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