12 accepted papers
This paper is concerned with the motion control problem of an aerial manipulator that consists of a quadrotor base and a manipulator. We propose a novel control strategy that incorporates a simplified model considering the critical dynamics, and a linear-MPC-based trajectory converter to ensure prec
Typical robot controllers assume firm ground, limiting their effectiveness in controlling robots on dynamic platforms such as trucks or ships. To address this limitation, we propose a reinforcement learning framework for robot locomotion on dynamic rigid platforms and a simulation in which 6-DoF dyn
Hand motion monitoring plays a crucial role in fields such as human-machine interaction and rehabilitation training. Currently, electronic sensors are commonly used for hand motion monitoring. However, they are confronted with issues such as susceptibility to electromagnetic interference and sweat s
The significant differences in the physical properties of air and water pose a substantial challenge for the development of hybrid aerial-aquatic vehicle (HAAV), which leading to increased prototype size, heavier thrusters, and reduced efficiency or under-actuation in one of the mediums. This letter
Domain generalization is a critical challenge for machine learning systems. Prior domain generalization methods focus on extracting domain-invariant features across several stationary domains to enable generalization to new domains. However, in non-stationary tasks where new domains evolve in an und…
Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications. The existing methodologies in the field primarily concentrate on constructing models that capture the relationship between utility function val…
Subset selection tasks, such as anomaly detection and compound selection in AI-assisted drug discovery, are crucial for a wide range of applications. Learning subset-valued functions with neural networks has achieved great success by incorporating permutation invariance symmetry into the architectur…
Deep reinforcement learning has achieved great success in many challenging domains. However, sample efficiency and safety issues still prevent from applying deep reinforcement learning directly in robotics. Sim-to-real transfer learning is one feasible solution to tackle these problems and address t
This paper investigates the stochastic moving target encirclement problem in a realistic setting. In contrast to typical assumptions in related works, the target in our work is non-cooperative and capable of escaping the circle containment by boosting its speed to maximum for a short duration. In ex…
Ankle dysfunction is common in the public following injuries, especially for stroke patients. Most of the current robotic ankle rehabilitation devices are driven by rigid actuators and have problems such as limited degrees of freedom, lack of safety and compliance, and poor flexibility. In this lett
This paper proposes a straightforward, intuitive deep learning approach for (biomedical) image segmentation tasks. Different from the existing dense pixel classification methods, we develop a novel multilevel aggregation network to directly regress the coordinates of the boundary of instances in an…
To solve the poor compliance and safety problems in current rehabilitation robots, a novel two-degrees-of-freedom (2-DOF) soft ankle rehabilitation robot driven by pneumatic muscles (PMs) is presented, taking advantages of the PM's inherent compliance and the parallel structure's high stiffness and…