18 accepted papers
Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years. However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research que…
While passive agents merely follow instructions, proactive agents align with higher-level objectives, such as assistance and safety by continuously monitoring the environment to determine when and how to act. However, developing proactive agents is hindered by the lack of specialized resources. To a…
This article proposes a whole-body impedance coordinative control framework for a wheel-legged humanoid robot to achieve adaptability on complex terrains while maintaining the robot's upper body stability. The framework contains a bi-level control strategy. The outer level is a variable-damping impe
Teleoperation has widely contributed to many applications. Consequently, the design of intuitive and ergonomic control interfaces for teleoperation has become crucial. The rapid advancement of Mixed Reality (MR) has yielded tangible benefits in human-robot interaction. MR provides an immersive envir
The wheel-bipedal robot has the advantages of both wheeled robots and legged robots, but as a cost, it is more challenging to perform flexible movements in various surroundings while keeping it balanced. The inaccurate dynamics of the robot makes the balance problem even more intractable. To solve t…
In this paper, we introduce DA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasp
This research presents a novel Collective Robotic Construction (CRC) system named RECCraft. The RECCraft hardware system is composed of the mobile manipulation vehicles, the cubic blocks, and the folding ramp blocks. Solid connection and easy removal of the blocks are achieved by an electropermanent…
Dynamic models play a key role in robot motion generation and control and the identification of inertial parameters is a critical component for obtaining an accurate dynamic model of a robot. This paper presents a novel iterative primitive shape division method for the inertia parameter identificati…
This paper presents a balance control technique for a novel wheel-legged robot. We first derive a dynamic model of the robot and then apply a linear feedback controller based on output regulation and linear quadratic regulator (LQR) methods to maintain the standing of the robot on the ground without…
This letter studies the adaptive optimal control problem for a wheel-legged robot in the absence of an accurate dynamic model. A crucial strategy is to exploit recent advances in reinforcement learning (RL) and adaptive dynamic programming (ADP) to derive a learning-based solution to adaptive optima
In this paper, a learning-based whole-body loco-motion controller is proposed, which enables quadruped robots to perform running in the style of real animals. We use a low-level controller based on multi-rigid body dynamics to calculate desired torques for each joint, while the high-level neural net…
Having the merits of chemical inertness and immunity to electromagnetic interference, light weight, small size, and softness, optical waveguides have attracted much attention in making tactile sensors recently. This paper presents a new design of waveguide using two layers of cores, one of which has…
In this paper, the gain scheduling technique is applied to design a balance controller for an autonomous bicycle with an inertia wheel. Previously, two different balance controllers are needed depending on whether the bicycle is stationary or dynamic. The switch between the two different controllers…
In this paper, nonlinear control techniques are exploited to balance an unmanned bicycle with enlarged stability domain. We consider two cases. For the first case when the autonomous bicycle is balanced by the flywheel, the steering angle is set to zero, and the torque of the flywheel is used as the…
In off-policy deep reinforcement learning, it is usually hard to collect sufficient successful experiences with sparse rewards to learn from. Hindsight experience replay (HER) enables an agent to learn from failures by treating the achieved state of a failed experience as a pseudo goal. However, not…
Semantic embeddings for images and sentences have been widely studied recently. The ability of deep neural networks on learning rich and robust visual and textual representations offers the opportunity to develop effective semantic embedding models. Currently, the state-of-the-art approaches in sema…
Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language description…
Estimating room impulse responses (RIRs) has a number of applications, including personalized audio, analyzing and improving acoustic behavior of concert halls, listening room compensation, sound source localization, and many others. RIRs have been estimated in essentially the same fashion for the l…