New Zealand


The multi-agent pickup and delivery (MAPD) problem, in which multiple agents iteratively carry materials without collisions, has received attention, but the conventional MAPD algorithms usually assume grid-like environments that are specifically designed such as automated warehouses. In these environments, many pickup/delivery locations are deployed where agents can stay for a long time, and the detours to avoid collision are plentiful because of freedom of movement. In contrast, a maze-like environment such as a rescue or a construction site generally has fewer pickup/delivery locations and the numbers may be unbalanced, resulting in many agents concentrating on these locations and inefficient operations. Moreover, the agents easily get stuck to avoid other agents and are deadlocked. Therefore, we propose a deadlock avoidance method, standby-based deadlock avoidance (SBDA), to improve transportation efficiency even in a maze-like environment. The SBDA is an algorithm using standby nodes, which is determined in real-time using the articulation-point-finding algorithm, and in which the agent is guaranteed to wait for any finite time. We demonstrated that our proposed method outperforms the conventional methods in maze-like restricted environments. Then, we analyze how the parameter for selecting standby nodes affects the performance.

AAMAS 2022

Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks

decentralized robot path planning

deadlock avoidance

multi-agent pickup and delivery tasks

multi-agent path finding

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

technical paper


Object pose estimation methods allow finding locations of objects in unstructured environments. This is a highly desired skill for autonomous robot manipulation as robots need to estimate the precise poses of the objects in order to manipulate them. In this paper, we investigate the problems of tactile pose estimation and manipulation for category-level objects. Our proposed method uses a Bayes filter with a learned tactile observation model and a deterministic motion model. Later, we train policies using deep reinforcement learning where the agents use the belief estimation from the Bayes filter. Our models are trained in simulation and transferred to the real world. We analyze the reliability and the performance of our framework through a series of simulated and real-world experiments and compare our method to the baseline work. Our results show that the learned tactile observation model can localize the pose of novel objects at 2-mm and 1-degree resolution for position and orientation, respectively. Furthermore, we experiment on a bottle opening task where the gripper needs to reach the desired grasp state to open the bottle cap.

Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation


Autonomous robots need to be able to efficiently compute long-horizon motion plans for achieving user desired tasks. Hierarchical planning approaches address this problem but require input state and action abstractions to decompose the overall problem and guide motion planners. This paper presents a new approach for bootstrapping the entire abstraction process by learning to predict the critical regions of a configuration space, using them to construct high-level actions as transitions between critical regions, and finally, by using the discovered actions to efficiently compute long-horizon motion plans. We also propose methods for auto-generating robot- and environment-specific deep neural network architectures for predicting high-dimensional critical regions in the configuration space. The overall algorithm is probabilistically complete and guaranteed to return sound abstractions of configuration spaces for holonomic robots. We evaluated our hierarchical approach based on learned abstractions against state-of-the-art sampling-based motion planners on a range of environments. The results show that our approach reduces the time required to compute motion plans by the magnitude of 10 compared to other motion planners.

Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning


Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of learning-based methods in multi-robot planning holds great promise as it enables us to offload the online computational burden of expensive centralized, yet optimal solvers, to an offline learning procedure. The hope is that by training a policy to copy an optimal pattern generated by a small-scale (centralized) system, we can transfer that policy to much larger, decentralized systems while maintaining near-optimal performance. Yet, a number of issues impede us from leveraging this idea to its full potential. This blue-sky paper elaborates some of the key challenges that remain.

The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts


Multi-agent reinforcement learning (MARL) requires coordination by some means of interaction between agents to efficiently solve tasks. Interaction graphs allow reasoning about joint actions based on the local structure of interactions, but they disregard the potential impact of an agent’s action on its neighbors’ behaviors, which could rapidly alter in dynamic settings. In this paper, we thus present a novel perspective on opponent modeling in domains with only local interactions using (level-1) Graph Probabilistic Recursive Reasoning (GrPR2). Unlike previous work on recursive reasoning, each agent iteratively best-responds to other agents’ policies over all possible local interactions. Agents’ policies are approximated via a variational Bayes scheme for capturing their uncertainties, and we prove that an induced variant of Q-learning converges under self-play when there exists only one Nash equilibrium. In cooperative settings, we further devise a variational lower bound on the likelihood of each agent's optimality. Opposed to other models, optimizing the resulting objective prevents each agent from attaining an unrealistic modelling of others, and yields an exact tabular Q-iteration method that holds convergence guarantees. Then, we deepen the recursion to level-k via Cognitive Hierarchy GrPR2 (GrPR2-CH), which lets each level-k player best-respond to a mixture of strictly lower levels in the hierarchy. We prove that: (1) level-3 reasoning is the optimal hierarchical level, maximizing each agent's expected return; and (2) the weak spot of the classical CH models is that 0-level is uniformly distributed, as it may introduce policy bias. Finally, we propose a practical actor-critic scheme, and illustrate that GrPR2-CH outperforms strong MARL baselines in the particle environment.

Optimizing Multi-Agent Coordination via Hierarchical Graph Probabilistic Recursive Reasoning


Centaurs are half-human, half-AI decision-makers where the AI's goal is to complement the human. To do so, the AI must be able to recognize the goals and constraints of the human and have the means to help them. We present a novel formulation of the interaction between the human and the AI as a sequential game where the agents are modelled using Bayesian best-response models. We show that in this case the AI's problem of helping bounded-rational humans make better decisions reduces to a Bayes-adaptive POMDP. In our simulated experiments, we consider an instantiation of our framework for humans who are subjectively optimistic about the AI's future behaviour. Our results show that when equipped with a model of the human, the AI can infer the human's bounds and nudge them towards better decisions. We discuss ways in which the machine can learn to improve upon its own limitations as well with the help of the human. We identify a novel trade-off for centaurs in partially observable tasks: for the AI's actions to be acceptable to the human, the machine must make sure their beliefs are sufficiently aligned, but aligning beliefs might be costly. We present a preliminary theoretical analysis of this trade-off and its dependence on task structure.

Best-Response Bayesian Reinforcement Learning with BA-POMDPs for Centaurs


Despite recent breakthroughs for learning a rich set of behaviors in simulated tasks, reinforcement learning agents are not yet in widespread use in the real world where rewards are naturally sparse. In fact, efficient exploration remains a key challenge in sparse-reward tasks as it requires quickly finding informative and task-relevant experiences. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch - when the learner’s goal deviates from the demonstrated behaviors. Moreover, we aim to obtain a policy that can accomplish a variety of goals guided by the same set of demonstrations (i.e. without additional human effort). We present a goal-conditioned method that leverages very small sets of goal-driven demonstrations to significantly accelerate learning. Crucially, we present the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We evaluate our framework on a set of robot control tasks. The present work outperforms prior imitation learning approaches in most of the tasks in terms of data efficiency and average scores while reducing the amount of human effort.

Goal-driven Active Reinforcement Learning with Human Teachers


This work addresses the problem of sensing the world: how to learn a multimodal representation of a reinforcement learning agent's environment that allows the execution of tasks under incomplete perceptual conditions. To address such problem, we argue for hierarchy in the design of representation models and contribute with a novel multimodal representation model, MUSE. The proposed model learns hierarchical representations: low-level modality-specific representations, encoded from raw observation data, and a high-level multimodal representation, encoding joint-modality information to allow robust state estimation. We employ MUSE as the sensory representation model of deep reinforcement learning agents provided with multimodal observations in Atari games. We perform a comparative study over different designs of reinforcement learning agents, showing that MUSE allows agents to perform tasks under incomplete perceptual experience with minimal performance loss. Finally, we evaluate the performance of MUSE in literature-standard multimodal scenarios with higher number and more complex modalities, showing that it outperforms state-of-the-art multimodal variational autoencoders in single and cross-modality generation.

How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents


Recent studies in multi-agent communicative reinforcement learning (MACRL) have demonstrated that multi-agent coordination can be greatly improved by allowing communication between agents. Meanwhile, adversarial machine learning (ML) have shown that ML models are vulnerable to attacks. Despite the increasing concern about the robustness of ML algorithms, how to achieve robust communication in multi-agent reinforcement learning has been largely neglected. In this paper, we systematically explore the problem of adversarial communication in MACRL. Our main contributions are threefold. First, we propose an effective method to perform attacks in MACRL, by learning a model to generate optimal malicious messages. Second, we develop a defence method based on message reconstruction, to maintain multi-agent coordination under message attacks. Third, we formulate the adversarial communication problem as a two-player zero-sum game and propose a game-theoretical method R-MACRL to improve the worst-case defending performance. Empirical results demonstrate that many state-of-the-art MACRL methods are vulnerable to message attacks, and our method can significantly improve their robustness.

Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning


Traditionally, Reinforcement Learning (RL) aims at deciding how to act optimally for an artificial agent. We argue that deciding when to act is equally important. As humans, we drift from default, instinctive or memorized behaviors to focused, thought-out behaviors when required by the situation. To enhance RL agents with this aptitude, we propose to augment the standard Markov Decision Process and make a new mode of action available: the lazy mode, which defers decision-making to a given default policy. In addition, we penalize non-lazy actions in order to enforce minimal effort and have agents focus on critical decisions only. We name the resulting formalism lazy-MDPs. We study the theoretical properties of lazy-MDPs, expressing value functions and characterizing greediness and optimal solutions. Then we empirically demonstrate that policies learned in lazy-MDPs are generally more interpretable and highlight the states where it is important for the agent to act. When the default policy is uniformly random, we observe that agents are still able to approximate or even to surpass classic DQN agents on some Atari games while only taking control at a fewer subset of the states.

Lazy-MDPs: Towards Interpretable RL by Learning When to Act


Imitation learning (IL) is a popular paradigm for training policies in robotic systems when specifying the reward function is difficult. However, despite the success of IL algorithms, they impose the somewhat unrealistic requirement that the expert demonstrations must come from the same domain in which a new imitator policy is to be learned. We consider a practical setting, where (i) state-only expert demonstrations from the real (deployment) environment are given to the learner, (ii) the imitation learner policy is trained in a simulation (training) environment whose transition dynamics is slightly different from the real environment, and (iii) the learner does not have any access to the real environment during the training phase beyond the batch of demonstrations given. Most of the current IL methods, such as generative adversarial imitation learning and its state-only variants, fail to imitate the optimal expert behavior under the above setting. By leveraging insights from the Robust reinforcement learning (RL) literature and building on recent adversarial imitation approaches, we propose a robust IL algorithm to learn policies that can effectively transfer to the real environment without fine-tuning. Furthermore, we empirically demonstrate on continuous-control benchmarks that our method outperforms the state-of-the-art state-only IL method in terms of the zero-shot transfer performance in the real environment and robust performance under different testing conditions.

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