We propose a framework that enables an agent to plan effectively under interaction uncertainty in decentralized multi-agent task and motion planning settings for cooperative manipulation tasks. In decentralized systems, each agent computes plans locally, based on local observations and assumptions on the other agents. Decentralization leads to each agent being uncertain about the actions and behavior of the other agents in the scene. We refer to this as ”interaction uncertainty”, as it arises from the implicit interaction between agents. This stems from limitations of the perception system and the inherent ambiguity of actions, meaning that an agent is not certain about the action being performed by the other agent(s). In other words, it is not certain if the other agents’ observed actions will obtain desired outcomes. In addition, an agent cannot predict the future behavior of the other agents or how this behavior is influenced by its own actions. The proposed framework addresses these two levels of interaction uncertainty in two-agent settings by leveraging the partial observability of the other agent’s short-term intent through ego’s perception system, and modeling the behavior of the other agent through an interactive MPD. The uncertainty about the other agent’s short- and long-term intent is represented using probabilistic effects of joint
actions. The focus of the thesis is on high-level, symbolic uncertainty in action recognition and preferences of the other agent, but may be extended to its low-level, geometric counterpart using existing methods in task and motion planning. Experiments are performed for different behavior models of the unknown agent, from the perspective of the protagonist or ego agent and are compared to baselines from sequential centralized planning. For most settings, the agents are able to eventually find the symbolic goal. Although the experiments are performed for a simple goal with two agents, the consistent convergence to the symbolic goal indicates that this approach may be extended to real-world settings with more complex ambiguity between actions and more agents acting collaboratively.