Vision-language models (VLMs) have tremendous potential for grounding language, and thus enabling language-conditioned agents (LCAs) to perform diverse tasks specified with text. This has motivated the study of LCAs based on reinforcement learning (RL) with rewards given by rendering images of an environment and evaluating those images with VLMs. If single-task RL is employed, such approaches are limited by the cost and time required to train a policy for each new task. Multi-task RL (MTRL) is a natural alternative, but requires a carefully designed corpus of training tasks and does not always generalize reliably to new tasks. Therefore, this paper introduces a novel decomposition of the problem of building an LCA: first find an environment configuration that has a high VLM score for text describing a task; then use a (pretrained) goal-conditioned policy to reach that configuration. We also explore several enhancements to the speed and quality of VLM-based LCAs, notably, the use of distilled models, and the evaluation of configurations from multiple viewpoints to resolve the ambiguities inherent in a single 2D view. We demonstrate our approach on the Humanoid environment, showing that it results in LCAs that outperform MTRL baselines in zero-shot generalization, without requiring any textual task descriptions or other forms of environment-specific annotation during training.

In few-shot imitation, an agent is given a few demonstrations of a previously unseen task, and must then successfully perform that task. We propose a novel approach to learning few-shot-imitation agents that we call demonstration-conditioned reinforcement learning (DCRL). Given a training set consisting of demonstrations, reward functions and transition distributions for multiple tasks, the idea is to define a policy that takes demonstrations and current state as inputs, and to train this policy to maximize the average of the cumulative reward over the set of training tasks. Compared to concurrent approaches, DCRL has several advantages, such as the ability to improve upon suboptimal demonstrations, to operate given state-only demonstrations, and to cope with a domain shift between the demonstrator and the agent. Moreover, we show that DCRL outperforms methods based on behaviour cloning by a large margin, on navigation tasks and on robotic manipulation tasks from the Meta-World benchmark.

Imitation learning has been considered as one of the promising approaches to enable a robot to acquire competencies. Recently, one-shot imitation learning has shown encouraging results for executing variations of initial conditions of a given task without requiring task-specific engineering. However, it remains inefficient for generalizing in variations of tasks involving different reward or transition functions. In this work, we aim at improving the generalization ability of demonstration based learning to unseen tasks that are significantly different from the training tasks. First, we introduce the use of transformer-based sequence-to-sequence policy networks trained from limited sets of demonstrations. Then, we propose to meta-train our model from a set of training demonstrations by leveraging optimization-based meta-learning. Finally, we evaluate our approach and report encouraging results using the recently proposed framework Meta-World which is composed of a large set of robotic manipulation tasks organized in various categories.