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Zihe Liu

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3 papers

2 author rows

IROS Conference 2025 Conference Paper

SMA-TENG Actuator with Tactile Sensing Capability

Yiping Zhang
Zihe Liu
Xi Xu
Jiaqi Jin
Boan Yang
Li Wen
Ziyu Ren

Shape memory alloy (SMA) is widely employed in developing actuators. However, the lack of sensing capabilities limits its application. This study presents a sensing-actuation integrated device based on SMA and triboelectric nanogenerator (TENG), achieving tactile sensing while maintaining the actuation performance. The proposed core-shell structure not only repurposes the SMA spring as a key component of actuation and sensing, but also effectively isolates the actuation current to prevent interference with the sensing signal. The aerogel-modified silicone composite layer is applied to the SMA to reduce temperature rise by 30. 56%, ensuring the sensing performance. With a rapid response time of less than 31 ms and stable sensing performance exceeding 2000 cycles, the SMA-TENG actuator reliably detects dynamically varying forces and bending. Additionally, it generates a maximum actuation force of 3. 21 N, which represents a 12. 2% increase compared to a standard SMA spring, due to the pre-stress introduced by the composite layer. Moreover, it can actuate a displacement of 7. 7 cm and exhibiting a power density of 7. 15 × 10 3 W/m 3 (at 0. 84 V, 6 A). Finally, we validate its haptic sensing capability during actuation, demonstrating its potential towards interactive robotic systems.

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IJCAI Conference 2024 Conference Paper

A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points

Zihe Liu
Jie Lu
Guangquan Zhang
Junyu Xuan

Deep reinforcement learning is used in various domains, but usually under the assumption that the environment has stationary conditions like transitions and state distributions. When this assumption is not met, performance suffers. For this reason, tracking continuous environmental changes and adapting to unpredictable conditions is challenging yet crucial because it ensures that systems remain reliable and flexible in practical scenarios. Our research introduces Behavior-Aware Detection and Adaptation (BADA), an innovative framework that merges environmental change detection with behavior adaptation. The key inspiration behind our method is that policies exhibit different global behaviors in changing environments. Specifically, environmental changes are identified by analyzing variations between behaviors using Wasserstein distances without manually set thresholds. The model adapts to the new environment through behavior regularization based on the extent of changes. The results of a series of experiments demonstrate better performance relative to several current algorithms. This research also indicates significant potential for tackling this long-standing challenge.

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UAI Conference 2024 Conference Paper

Functional Wasserstein Variational Policy Optimization

Junyu Xuan
Mengjing Wu
Zihe Liu
Jie Lu 0001

Variational policy optimization has become increasingly attractive to the reinforcement learning community because of its strong capability in uncertainty modeling and environment generalization. However, almost all existing studies in this area rely on Kullback{–}Leibler (KL) divergence which is unfortunately ill-defined in several situations. In addition, the policy is parameterized and optimized in weight space, which may not only bring additional unnecessary bias but also make the policy learning harder due to the complicatedly dependent weight posterior. In the paper, we design a novel functional Wasserstein variational policy optimization (FWVPO) based on the Wasserstein distance between function distributions. Specifically, we firstly parameterize policy as a Bayesian neural network but from a function-space view rather than a weight-space view and then propose FWVPO to optimize and explore the functional policy posterior. We prove that our FWVPO is a valid variational Bayesian objective and also guarantees the monotonic expected reward improvement under certain conditions. Experimental results on multiple reinforcement learning tasks demonstrate the efficiency of our new algorithm in terms of both cumulative rewards and uncertainty modeling capability.

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