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Ken Chen

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4 papers

2 author rows

EAAI Journal 2024 Journal Article

Local connection reinforcement learning method for efficient robotic peg-in-hole assembly

Yuhang Gai
Jiwen Zhang
Dan Wu
Ken Chen

Traditional robotic peg-in-hole assembly methods rely on complex contact state analysis. Reinforcement learning (RL) is gradually becoming a preferred method of controlling robotic peg-in-hole assembly tasks. However, the training process of RL is quite time-consuming because RL methods are always globally connected, which means all state components are assumed to be the input of policies for all action components, thus increasing action space and state space to be explored. In this paper, we first define continuous space serialized Shapley value (CS3) and construct a connection graph to clarify the correlativity of action components on state components. Then we propose a local connection reinforcement learning (LCRL) method based on the connection graph, which eliminates the influence of irrelevant state components on the selection of action components. The simulation and experiment results demonstrate that the LCRL method can achieve the same average reward as the traditional RL method in only 49% of episodes. In the final episode, the LCRL method's final reward is 35% higher than that of the traditional RL method, which guarantees the rapidity and stability of the assembly process.

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EAAI Journal 2024 Journal Article

Robotic assembly control reconfiguration based on transfer reinforcement learning for objects with different geometric features

Yuhang Gai
Bing Wang
Jiwen Zhang
Dan Wu
Ken Chen

Robotic force-based compliance control is a preferred approach to achieve high-precision assembly tasks. When the geometric features of assembly objects are asymmetric or irregular, reinforcement learning (RL) agents are gradually incorporated into the compliance controller to adapt to complex force-pose mapping which is hard to model analytically. Since force-pose mapping is strongly dependent on geometric features, a compliance controller is only optimal for current geometric features. To reduce the learning cost of assembly objects with different geometric features, this paper is devoted to answering how to reconfigure existing controllers for new assembly objects with different geometric features. In this paper, model-based parameters are first reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL). Then the RL agent is transferred based on the proposed Weighted Dimensional Policy Distillation (WDPD) method. The experiment results demonstrate that the control reconfiguration method costs less time and achieves better control performance, which confirms the validity of proposed methods.

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AAAI Conference 2020 Conference Paper

Learning to Auto Weight: Entirely Data-Driven and Highly Efficient Weighting Framework

Zhenmao Li
Yichao Wu
Ken Chen
Yudong Wu
Shunfeng Zhou
Jiaheng Liu
Junjie Yan

Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters. In this paper, we propose a novel example weighting framework called Learning to Auto Weight (LAW). The proposed framework ﬁnds step-dependent weighting policies adaptively, and can be jointly trained with target networks without any assumptions or prior knowledge about the dataset. It consists of three key components: Stage-based Searching Strategy (3SM) is adopted to shrink the huge searching space in a complete training process; Duplicate Network Reward (DNR) gives more accurate supervision by removing randomness during the searching process; Full Data Update (FDU) further improves the updating efﬁciency. Experimental results demonstrate the superiority of weighting policy explored by LAW over standard training pipeline. Compared with baselines, LAW can ﬁnd a better weighting schedule which achieves much more superior accuracy on both biased CIFAR and ImageNet.

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ICRA Conference 2002 Conference Paper

Control System Design of THBIP-I Humanoid Robot

Mingguo Zhao
Li Liu
Jingsong Wang
Ken Chen
Jiandong Zhao
Kai Xu

Describes the progress of the control system design and implementation of the THBIP-I humanoid robot. The robot has 32 degrees of freedom and each joint is driven by a brushless DC electronic motor. Screw/nuts transmission mechanism is adapted in some joints of lower limbs to achieve compact and good dynamic performance. The control system of the robot has four subsystems: remote brain work station, mobile controller, distributed control units and sensor processing unit. At the present state, the lower limbs and upper limbs have been built and tested with off line gait planning. The distributed control units use PID schemes to servo the pre-generated joint trajectories. Under this architecture, the robot can perform stable walking with 30 centimeters step at 20 second per step.

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