# Bimanual Manipulation

The bottleneck in long-horizon bimanual manipulation is not only per-hand skill but coordination of the two arms in space and time. BiPOP partitions the workspace by arm reachability, has a large language model write the natural-language instruction and scene information into a PDDL problem, and lets a temporal planner produce a partial-order plan with concurrent scheduling. On 100 randomized instances each of ServeFruit, ServeWater and StackBlock, BiPOP reaches success rates of 100%, 100% and 95%, the only method at or above 95% in every domain, and the full pipeline completes ServeWater end to end on the real bimanual robot NICOL.

The bottleneck in long-horizon bimanual manipulation is not only per-hand skill but coordination of the two arms in space and time. BiPOP partitions the workspace by arm reachability, has a large language model write the natural-language instruction and scene information into a PDDL problem, and lets a temporal planner produce a partial-order plan with concurrent scheduling. On 100 randomized instances each of ServeFruit, ServeWater and StackBlock, BiPOP reaches success rates of 100%, 100% and 95%, the only method at or above 95% in every domain, and the full pipeline completes ServeWater end to end on the real bimanual robot NICOL.

Treating one successful demonstration as an editable skeleton, a learned editor decides when to switch between object-centric stages and how to correct each stage geometrically from sparse success rewards, reaching 92.7% average success on 15 Meta-World tasks and 98.4% average edit success on 9 DexMimicGen tasks.

Treating one successful demonstration as an editable skeleton, a learned editor decides when to switch between object-centric stages and how to correct each stage geometrically from sparse success rewards, reaching 92.7% average success on 15 Meta-World tasks and 98.4% average edit success on 9 DexMimicGen tasks.

Large-scale robot datasets are overwhelmingly monocular and single-arm, and capturing bimanual stereo demonstrations would need stereo rigs plus re-teleoperation for every task. StereoEngine offers two complementary routes: when a real monocular video exists, it warps the left view into the right view using monocular depth and inpaints the regions the left camera never observed with a video diffusion model; when no real video exists, it renders a stereo pair in simulation and generates both photorealistic views jointly by tiling them into one frame. On an AgileX PiPER bimanual platform, mid-training or fine-tuning policies on this synthetic data succeeds on 50/60 trials across three tasks versus 36/60 without mid-training, reaching 18/20 on transparent-bottle grasping.

Large-scale robot datasets are overwhelmingly monocular and single-arm, and capturing bimanual stereo demonstrations would need stereo rigs plus re-teleoperation for every task. StereoEngine offers two complementary routes: when a real monocular video exists, it warps the left view into the right view using monocular depth and inpaints the regions the left camera never observed with a video diffusion model; when no real video exists, it renders a stereo pair in simulation and generates both photorealistic views jointly by tiling them into one frame. On an AgileX PiPER bimanual platform, mid-training or fine-tuning policies on this synthetic data succeeds on 50/60 trials across three tasks versus 36/60 without mid-training, reaching 18/20 on transparent-bottle grasping.

The authors introduce ExStereo, a stereo module that uses a frozen stereo foundation model to reconstruct a metric point cloud from stereo image pairs, renders it into four orthographic views (Top, Front, Left, Right) as an explicit stereo representation, encodes them with a ViT, and lets action tokens selectively read them through action-stereo cross-attention; after mid-training on large-scale stereo data with a masked autoencoding objective and freezing the module during post-training, it raises average success on ten RoboTwin simulation tasks from 51.2% to 76.4% and real-world success on three bimanual PiPER tasks from 16/60 to 50/60, outperforming point-cloud and implicit-stereo baselines.

The authors introduce ExStereo, a stereo module that uses a frozen stereo foundation model to reconstruct a metric point cloud from stereo image pairs, renders it into four orthographic views (Top, Front, Left, Right) as an explicit stereo representation, encodes them with a ViT, and lets action tokens selectively read them through action-stereo cross-attention; after mid-training on large-scale stereo data with a masked autoencoding objective and freezing the module during post-training, it raises average success on ten RoboTwin simulation tasks from 51.2% to 76.4% and real-world success on three bimanual PiPER tasks from 16/60 to 50/60, outperforming point-cloud and implicit-stereo baselines.

The work introduces EyeRobot 2.0, a framework in which active gaze physically swivels two eye viewpoints to center on a 3D fixation point and processes the resulting images foveally by allocating more visual tokens to image centers, enabling fine-grained bimanual manipulation with only a single stereo camera; across 7 real-world and 6 simulated tasks and over 1000 physical and 1800 simulated robot trials, it outperforms passive stereo policies by 40% in real and 20% in sim, matches ego + wrist policies when wrist views are clear (69% vs. 64%), and more than doubles their success when grasped objects occlude the wrist cameras (48% vs. 22%).

The work introduces EyeRobot 2.0, a framework in which active gaze physically swivels two eye viewpoints to center on a 3D fixation point and processes the resulting images foveally by allocating more visual tokens to image centers, enabling fine-grained bimanual manipulation with only a single stereo camera; across 7 real-world and 6 simulated tasks and over 1000 physical and 1800 simulated robot trials, it outperforms passive stereo policies by 40% in real and 20% in sim, matches ego + wrist policies when wrist views are clear (69% vs. 64%), and more than doubles their success when grasped objects occlude the wrist cameras (48% vs. 22%).