Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning

Abstract: This paper tackles the challenge of learning multi-goal dexterous hand manipulation tasks using model-based Reinforcement Learning. We propose Goal-Conditioned Probabilistic Model Predictive Control (GC-PMPC) by designing probabilistic neural network ensembles to describe the high-dimensional dexterous hand dynamics and introducing an asynchronous MPC policy to meet the control frequency requirements in real-world dexterous hand systems. Extensive evaluations on four simulated Shadow Hand manipulation scenarios with randomly generated goals demonstrate GC-PMPC's superior performance over state-of-the-art baselines. It successfully drives a cable-driven Dexterous hand, DexHand 021 with 12 Active DOFs and 5 tactile sensors, to learn manipulating a cubic die to three goal poses within approximately 80 minutes of interactions, demonstrating exceptional learning efficiency and control performance on a cost-effective dexterous hand platform.

• The paper introduces Goal-Conditioned Probabilistic Model Predictive Control (GC-PMPC), a probabilistic model-based reinforcement learning method designed for efficient multi-goal dexterous hand manipulation and improved sim-to-real transfer.
• GC-PMPC achieves superior learning efficiency and higher success rates compared to previous model-free and model-based baselines in simulated dexterous hand tasks.
• The method successfully manipulated multiple die orientations on a real-world, low-cost robotic hand (DexHand 021) within approximately 80 minutes, demonstrating practical real-world applicability.

Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning

The paper "Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning" introduces a novel approach named Goal-Conditioned Probabilistic Model Predictive Control (GC-PMPC) for enhancing the learning efficiency and control performance of dexterous robotic hands in multi-goal manipulation tasks. Specifically, the research addresses the significant challenge of transferring learned optimal control policies concerning dexterous hand manipulation from simulation environments to real-world hardware platforms.

Core Contributions and Methodology

The cornerstone of this method lies in leveraging Probabilistic Model-based Reinforcement Learning (MBRL) to systematically derive control policies in tasks with complex dynamics and sparse reward signals. The proposed GC-PMPC methodology highlights several key innovations:

1. Probabilistic Neural Network Ensembles: GC-PMPC emphasizes model expressiveness and generalization efficiency by incorporating probabilistic neural network ensembles augmented with Batch Normalization to alleviate the issue of non-uniform data distributions.
2. Asynchronous MPC Policy: Introducing an asynchronous mechanism, the paper addresses the computational challenge of traditional MPC frequency mismatches with real-world hand systems. This mechanism decouples the control frequency requirements, thus enhancing the real-time action execution efficiency.
3. State Smoothing Mechanism: To counteract the effects of model prediction variance, GC-PMPC employs a state smoothing mechanism within the MPC policy, aiming to reduce policy instability caused by sudden state changes.

Experimental Results

The effectiveness of GC-PMPC was validated through extensive evaluations on both simulated and real-world dexterous hand systems:

• Simulated Environments: Utilizing four distinct manipulation scenarios with the Shadow Hand platform, GC-PMPC demonstrated superior learning efficiency, achieving higher success rates than model-free baselines such as SAC and DDPG with HER, and model-based baselines including PETS and TDMPC. The proposed method achieved proficiency in multi-objective manipulation tasks within significantly reduced timeframes.
• Real-world Implementation: The application of GC-PMPC on a low-cost DexHand 021 showcased proficient manipulation of multiple die orientations within an approximate duration of 80 minutes, underscoring its potential for deployment in resource-constrained environments.

Implications and Future Directions

The implications of this research extend to both practical applications and theoretical advancements in robotic control. GC-PMPC offers an efficient framework capable of bypassing common barriers associated with MBRL, particularly in handling high-dimensional control systems with multiple objectives. Its robustness in adapting learned policies between simulated and real-world conditions suggests promising developments in improving the generalization capabilities of AI agents.

Future research may focus on extending the state smoothing mechanism and the neural network architecture to further optimize control performance under dynamic environmental constraints. Applying these principles to other forms of dexterous manipulation and cross-platform transfer learning also represents a valuable area for exploration. The results invite further inquiry into probabilistic MBRL methodologies, hinting at new paradigms for intelligence frameworks in autonomous robotics.