Heterogeneous robotic hands work together for better object handling
Embodiment-Aware Planning for Heterogeneous Bimanual Manipulation
Robotics
Summary
Robots often use simple grippers like suction cups or jaws to pick up and move things, but each type has limits. This paper shows how two different grippers on separate robot arms can cooperate to do tasks no single gripper can handle alone. The authors use a plan that decides when to use one arm or both based on the task and gripper capabilities, guided by a human demonstration. Tests show this approach works better for difficult tasks involving moving and placing objects.
What this means in practice
- •For industrial robot integrators: Improve robot handling in factories by coordinating multiple simple grippers for complex assembly tasks that single grippers cannot perform.
- •For robotics software developers: Create planning systems that assign tasks dynamically between robot arms with different grippers based on object handling needs and gripper compatibility.
Authors
Sukhvansh Jain, Anurag Maurya, Shivam Vats, Narendra Kumar Dhar, Ravi Prakash
Abstract
Many manipulation tasks that are effortless for dexterous human hands remain challenging for robotic systems equipped with simple, task-specialized grippers. Industrial platforms commonly rely on end-effectors such as suction cups or parallel-jaw grippers, yet no single mode can reliably satisfy the diverse constraints encountered across different phases of complex manipulation tasks, including pickup, reorientation, insertion, and constrained placement. In this work, we investigate whether complementary simple grippers within a heterogeneous bimanual setup can collectively realize tasks that exceed the capability of any individual gripper alone. Given a single human demonstration specifying the desired object motion, we formulate embodiment-aware execution as a finite-horizon optimization over arm-trajectory assignments. At each segment, the planner determines whether single-arm manipulation or a coordinated dual arm handover is required, jointly reasoning over heterogeneous grasp feasibility, motion constraints, manipulability, and cross-gripper compatibility. Exploiting this formulation, we compute globally consistent assignments with explicit switching regularization. Experiments on a dual Franka FR3 platform equipped with suction and parallel-jaw grippers demonstrate improved feasibility and reliability in tasks that are infeasible under single-gripper execution.