MiniBEE: A New Form Factor for Compact Bimanual Dexterity

TL;DR

MiniBEE introduces a compact bimanual end-effector composed of two 3+ DOF arms connected in a chain to preserve full relative gripper dexterity (6+ DOF). A kinematic dexterity (KD) metric evaluates how many sampled relative poses are IK-feasible, guiding design toward an 8-DOF configuration that balances footprint and performance, comparable to larger systems like ALOHA. The system supports wearable kinesthetic demonstrations with self-tracked gripper poses and can be mounted on a standard robot arm to exploit extended reach, trained end-to-end via imitation learning using a diffusion policy. Experimental results across three tasks demonstrate robust dexterous manipulation learned from wearable data, achieving high success rates and highlighting MiniBEE’s practicality for real-world, low-footprint bimanual control.

Abstract

Bimanual robot manipulators can achieve impressive dexterity, but typically rely on two full six- or seven- degree-of-freedom arms so that paired grippers can coordinate effectively. This traditional framework increases system complexity while only exploiting a fraction of the overall workspace for dexterous interaction. We introduce the MiniBEE (Miniature Bimanual End-effector), a compact system in which two reduced-mobility arms (3+ DOF each) are coupled into a kinematic chain that preserves full relative positioning between grippers. To guide our design, we formulate a kinematic dexterity metric that enlarges the dexterous workspace while keeping the mechanism lightweight and wearable. The resulting system supports two complementary modes: (i) wearable kinesthetic data collection with self-tracked gripper poses, and (ii) deployment on a standard robot arm, extending dexterity across its entire workspace. We present kinematic analysis and design optimization methods for maximizing dexterous range, and demonstrate an end-to-end pipeline in which wearable demonstrations train imitation learning policies that perform robust, real-world bimanual manipulation.

Figures (5)

• Figure 1: Overview. The MiniBEE is a bimanual end-effector consisting of two compact arms equipped with grippers. The kinematic chain connecting the two grippers has sufficient degrees of freedom so that, with careful kinematic optimization, the two grippers have full manipulability w.r.t. each other, enabling high dexterity. The MiniBEE is compact enough to be worn and operated for kinesthetic data collection (left) for behavioral cloning, and the trained policies can be deployed with the MiniBEE mounted on a standard robot arm (right), giving the overall system a large reach to combine with its dexterity.
• Figure 2: MiniBEE concept. We use a bimanual system designed to mount on a larger robot arm. When designing the MiniBEE, we rely on a kinematic convention which treats one gripper as the base coordinate frame (the "fixed" gripper) and the other as the tip of the chain (the "free" gripper). Rather than computing each gripper’s pose relative to a robot tool frame (blue), we can then solve kinematic queries directly between the two grippers, relying on the fact that the complete kinematic chain connecting them (red) has sufficient DOFs for relative dexterity.
• Figure 3: We evaluate the kinematic dexterity between two grippers of a bi-manual kinematic chain. One of the grippers in the chain, dubbed the fixed gripper, serves as the base of our coordinate frame. We then set a desired volume for a workspace around the fixed gripper uniformly sample a set of points in this volume. We use each of these points as a desired position for the "free" gripper, oriented such that it points towards the fixed gripper (bottom image). If an IK solution for this query exists, obeys joint limits and self-collisions, and does not place the robot near a singularity, we consider the query point as reachable. The ratio of reachable to non-reachable points in the workspace is used as a metric for the relative kinematic dexterity between two grippers that are connected by a single kinematic chain.
• Figure 4: Kinematic analysis for possible MiniBEE configurations as well as a state of the art bimanual system. For each configuration, we follow the procedure outlined in Algorithm 1 to compute the KD-metric for a desired fixed rectangular workspace with a side length of 20 cm. Successful desired points in the workspace are shown in green, points skipped due to IK solutions being near singularities are shown in blue, and points where there are no collision-free IK solutions are shown in red. The 8-DOF MiniBEE performed the best, as expected, and was comparable in performance to traditional bimanual systems that use two full mobility arms. Reducing the configuration to 7 and 6-DOF reduced the dexterity score significantly. For all experiments shown in the paper, we used the 8-DOF configuration, as it provided the best balance between complexity, size, and performance.
• Figure 5: We conduct a set of three manipulation experiments with MiniBEE. The first task, shown in the first row, is to grasp an object from the table, reorient it by handing it off to the other gripper, and place it on the table in a different pose. The second task, shown in the second row, is for MiniBEE to pick up a pair of folded sunglasses from the table, unfold them with the other gripper, and then place on a display rack. The third task is to pick up a closed pill bottle, unscrew the cap, pick up a bowl with the other gripper, and then empty the contents of the bottle into the bowl. MiniBEE performs these task with a high rate of success, and overall this set of experiments highlights the ability for our design to achieve bi-manual dexterity.