Robot hand is essential for a fully functional robot and designing a good robot hand is a sophisticated job that challenges the designer’s knowledge and experience. This paper presents a computational framework for automatic optimal robot hand design based on reinforcement learning (RL), which considers desired grasping tasks, grasp control strategies, and performance quality measures altogether. The RL-based framework intends to grow finger joints with different types and link lengths at different positions from null. Then, the reward function for such a growing action is defined in terms of quality indexes of the generated robot hand to perform desired grasping tasks under expected control strategies. To demonstrate the effectiveness of this framework, in this paper we set the desired task to simply grasping objects of three primitive shapes (i.e., box, cylinder, and sphere) with predefined hand positions and strategies to close fingers to achieve grasps for each object. The force closure condition, quantitative stability indexes, and energy consumption of grasps as well as some penalty terms are used to assemble the reward function. Through simulation and practical prototype experiments, we show that capable robot hands can be automatically generated by the proposed framework. Potential factors that affect the output of the framework and deserve further exploration are also discussed.

In human-robot collaboration, it is crucial for the robot to make its intentions clear and predictable to the human partners. Inspired by the mutual learning and adaptation of human partners, we suggest an actor-critic approach for a legible robot motion planner. This approach includes two neural networks and a legibility evaluator: 1) A policy network based on deep reinforcement learning (DRL); 2) A Recurrent Neural Networks (RNNs) based sequence to sequence (Seq2Seq) model as a motion predictor; 3) A legibility evaluator that maps motion to legible reward. Through a series of human-subject experiments, we demonstrate that with a simple handicraft function and no real-human data, our method lead to improved collaborative performance against a baseline method and a non-prediction method.

It is well-known that a deep understanding of coworkers' behavior and preference is important for collaboration effectiveness. In this work, we present a method to accomplish smooth human-robot collaboration in close proximity by taking into account the human's behavior while planning the robot's trajectory. In particular, we first use an occupancy map to summarize human's movement preference over time, and such prior information is then considered in an optimization-based motion planner via two cost items: 1) avoidance of the workspace previously occupied by human, to eliminate the interruption and to increase the task success rate; 2) tendency to keep a safe distance between the human and the robot to improve the safety. In the experiments, we compare the collaboration performance among planners using different combinations of human-aware cost items, including the avoidance factor, both the avoidance and safe distance factor, and a baseline where no human-related factors are considered. The trajectories generated are tested in both simulated and real-world environments, and the results show that our method can significantly increase the collaborative task success rates and is also human-friendly.