RoMAT: Role-based multi-agent transformer for generalizable heterogeneous cooperation.

Multi-task multi-agent systems (MASs) are challenging to model because they involve heterogeneous agents with different behavior patterns that need to cooperate across various tasks. Existing networks for single-agent policies are not suitable for this setting, as they cannot share policies among agents without losing task-specific performance. We propose a novel framework called Role-based Multi-Agent Transformer (RoMAT), which uses a sequence modeling technique and a role-based actor to enable agents to adapt to different tasks and roles in MASs.

Bi-DexHands: Towards Human-Level Bimanual Dexterous Manipulation.

Achieving human-level dexterity in robotics remains a critical open problem. Even simple dexterous manipulation tasks pose significant difficulties due to the high number of degrees of freedom and the need for cooperation among heterogeneous agents (e.g.

[Gynostemma pentaphyllum saponins alleviate non-alcoholic fatty liver disease in rats by regulating intestinal flora and short-chain fatty acid metabolism].

This study aimed to explore the effects of Gynostemma pentaphyllum saponins(GPs) on non-alcoholic fatty liver disease(NAFLD) induced by high-fat diet in rats and reveal the underlying mechanism. The NAFLD model rats were prepared with high-fat diet. Forty male Sprague Dawley(SD) rats were randomly assigned into the control group, model group, and low-, moderate-, and high-dose GPs(50, 100, and 150 mg·kg~(-1), respectively) groups.

AD-VAT+: An Asymmetric Dueling Mechanism for Learning and Understanding Visual Active Tracking.

Visual Active Tracking (VAT) aims at following a target object by autonomously controlling the motion system of a tracker given visual observations. To learn a robust tracker for VAT, in this article, we propose a novel adversarial reinforcement learning (RL) method which adopts an Asymmetric Dueling mechanism, referred to as AD-VAT. In the mechanism, the tracker and target, viewed as two learnable agents, are opponents and can mutually enhance each other during the dueling/competition: i.

End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning.

We study active object tracking, where a tracker takes visual observations (i.e., frame sequences) as input and produces the corresponding camera control signals as output (e.