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Christian Goerick

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26 papers

2 author rows

TMLR Journal 2026 Journal Article

Task-Specific Exploration in Meta-Reinforcement Learning via Task Reconstruction

Radu Stoican
Angelo Cangelosi
Christian Goerick
Thomas H Weisswange

Reinforcement learning trains policies specialized for a single task. Meta-reinforcement learning (meta-RL) improves upon this by leveraging prior experience to train policies for few-shot adaptation to new tasks. However, existing meta-RL approaches often struggle to explore and learn tasks effectively. We introduce a novel meta-RL algorithm that learns to learn task-specific exploration policies for sample-efficient few-shot adaptation. We achieve this through task reconstruction, an original method for learning to identify and collect small but informative datasets from tasks. To leverage these datasets, we also propose learning a meta-reward that encourages policies to learn to adapt. Empirical evaluations demonstrate that our algorithm achieves higher returns than existing meta-RL methods. Additionally, we show that even with full task information, adaptation is more challenging than previously assumed. However, policies trained with our meta-reward adapt to new tasks successfully.

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AIJ Journal 2020 Journal Article

Reasoning about uncertain parameters and agent behaviors through encoded experiences and belief planning

Akinobu Hayashi
Dirk Ruiken
Tadaaki Hasegawa
Christian Goerick

Robots are expected to handle increasingly complex tasks. Such tasks often include interaction with objects or collaboration with other agents. One of the key challenges for reasoning in such situations is the lack of accurate models that hinders the effectiveness of planners. We present a system for online model adaptation that continuously validates and improves models while solving tasks with a belief space planner. We employ the well known online belief planner POMCP. Particles are used to represent hypotheses about the current state and about models of the world. They are sufficient to configure a simulator to provide transition and observation models. We propose an enhanced particle reinvigoration process that leverages prior experiences encoded in a recurrent neural network (RNN). The network is trained through interaction with a large variety of object and agent parametrizations. The RNN is combined with a mixture density network (MDN) to process the current history of observations in order to propose suitable particles and models parametrizations. The proposed method also ensures that newly generated particles are consistent with the current history. These enhancements to the particle reinvigoration process help alleviate problems arising from poor sampling quality in large state spaces and enable handling of dynamics with discontinuities. The proposed approach can be applied to a variety of domains depending on what uncertainty the decision maker needs to reason about. We evaluate the approach with experiments in several domains and compare against other state-of-the-art methods. Experiments are done in a collaborative multi-agent and a single agent object manipulation domain. The experiments are performed both in simulation and on a real robot. The framework handles reasoning with uncertain agent behaviors and with unknown object and environment parametrizations well. The results show good performance and indicate that the proposed approach can improve existing state-of-the-art methods.

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ICRA Conference 2019 Conference Paper

Online adaptation of uncertain models using neural network priors and partially observable planning

Akinobu Hayashi
Dirk Ruiken
Christian Goerick
Tadaaki Hasegawa

One of the key challenges in realizing a robot that is capable of completing a variety of manipulation tasks in the real world is the need to utilize sufficiently compact and rich world models. If the assumed prediction model does not match real observations, planning systems are unable to perform properly. We propose a system that corrects the models based on information collected from the robot's sensors. We encode prior experiences in a neural network to generate possible parameters of the models for a physics engine from real observations. An online POMDP solver is used to plan actions to complete the task while progressively validating and improving the models. We perform experiments in simulations and on a real robot. The results show that this approach appropriately clarifies observed environments, can handle dynamics with discontinuities, and with increasing domain complexity achieves a better success rate than baseline methods.