CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks (2112.03227v4)

Abstract: General-purpose robots coexisting with humans in their environment must learn to relate human language to their perceptions and actions to be useful in a range of daily tasks. Moreover, they need to acquire a diverse repertoire of general-purpose skills that allow composing long-horizon tasks by following unconstrained language instructions. In this paper, we present CALVIN (Composing Actions from Language and Vision), an open-source simulated benchmark to learn long-horizon language-conditioned tasks. Our aim is to make it possible to develop agents that can solve many robotic manipulation tasks over a long horizon, from onboard sensors, and specified only via human language. CALVIN tasks are more complex in terms of sequence length, action space, and language than existing vision-and-language task datasets and supports flexible specification of sensor suites. We evaluate the agents in zero-shot to novel language instructions and to novel environments and objects. We show that a baseline model based on multi-context imitation learning performs poorly on CALVIN, suggesting that there is significant room for developing innovative agents that learn to relate human language to their world models with this benchmark.

An Analysis of CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks

The paper "CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks" presented by Oier Mees et al. introduces a new benchmark named CALVIN. This benchmark is a significant addition to the field of robotics, particularly focusing on the integration of NLP and long-horizon robotic manipulation tasks. The development of CALVIN addresses the necessity for robots to not only understand human language but also execute long-horizon tasks based on these instructions in varied environments.

Overview of CALVIN

CALVIN is designed to enable the development of agents capable of executing multiple robotic manipulation tasks via natural language commands. It leverages simulated environments where robotic agents are tasked with long-horizon tasks specifically conditioned by linguistic input. The focus on language-conditioned tasks within simulated environments marks CALVIN distinctively from existing benchmarks that primarily focus on task-specific goals without the complexity introduced by interpreting and acting on natural language instructions.

Key Features

A key feature of CALVIN is its structured setup across four different manipulation environments that share core structuring but differ in component configurations such as object placements and textures. This setup promotes the evaluation of generalization abilities across environments and unseen tasks. CALVIN's synthetic datasets, which include approximately 24 hours of recorded unstructured robot interaction data coupled with human-crowdsourced language instructions, provide a robust platform for zero-shot learning and cross-environment generalization. These datasets are indispensable for training agents in a way that mimics realistic interaction behaviors, unbound by fixed task constraints.

The benchmark permits sensor configurations incorporating both visual data from static and gripper-mounted cameras and proprioceptive feedback data, driving towards comprehensive sensor integration for real-world applicability. CALVIN uniquely enables the development of agents using both absolute and relative action spaces, introducing additional flexibility in agent action modeling.

Baseline and Evaluation Protocol

The authors introduce a baseline model utilizing multi-context imitation learning (MCIL), which has shown prior efficacy in language-conditioned tasks. The complexity introduced by long-horizon tasks typically necessitates advanced forms of imitation or reinforcement learning, foregrounding CALVIN as a challenging benchmark for contemporary and future methods.

The evaluation protocol of CALVIN is twofold – Multi-Task Language Control (MTLC) evaluates single task performance, while the more challenging Long-Horizon MTLC assesses sequential task execution up to sequences of five. By evaluating models in multiple environments and zero-shot settings, the benchmark seeks not only to test task execution proficiency but also adaptability and generalization capability.

Results and Implications

Initial results indicate that the baseline model performs moderately with the MTLC setting but significantly underperforms with long-horizon sequences and generalization tasks in unseen environments. This shortfall highlights considerable opportunities for advancement in control policies grounded in natural language understanding and long-horizon reasoning.

The paper posits that further innovations and integrations in multimodal sensor processing, enhanced imitation learning techniques, domain adaptation strategies, and enriched language grounding could improve baseline performance. The comprehensive evaluation offered by CALVIN sets a foundation for the emergence of more sophisticated, adaptable, and scalable robotics systems.

Future Directions

The research underscores the potential of CALVIN in fostering developments in language-driven robotics. Ongoing research avenues could include extending sensor modalities, refining benchmarks to include more complex environments and tasks, and encouraging community-driven benchmark expansions. The exploration of novel architectures and frameworks that better encapsulate humanlike task flexibility and abstract concept generalization remains critical.

In conclusion, CALVIN is positioned as a pivotal contribution to the intersection of robotics and natural language processing, with considerable implications for the future of autonomous robot systems interacting seamlessly within human-centered environments.