Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction (1710.09483v1)

Abstract: This paper presents a method for constructing human-robot interaction policies in settings where multimodality, i.e., the possibility of multiple highly distinct futures, plays a critical role in decision making. We are motivated in this work by the example of traffic weaving, e.g., at highway on-ramps/off-ramps, where entering and exiting cars must swap lanes in a short distance---a challenging negotiation even for experienced drivers due to the inherent multimodal uncertainty of who will pass whom. Our approach is to learn multimodal probability distributions over future human actions from a dataset of human-human exemplars and perform real-time robot policy construction in the resulting environment model through massively parallel sampling of human responses to candidate robot action sequences. Direct learning of these distributions is made possible by recent advances in the theory of conditional variational autoencoders (CVAEs), whereby we learn action distributions simultaneously conditioned on the present interaction history, as well as candidate future robot actions in order to take into account response dynamics. We demonstrate the efficacy of this approach with a human-in-the-loop simulation of a traffic weaving scenario.

Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction

The paper on "Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction" introduces a novel framework to enhance human-robot interaction (HRI) in scenarios characterized by inherent multimodality and uncertainty, specifically focusing on traffic weaving situations encountered at highway ramps. This work addresses the complexity of predicting human behavior and planning robot responses in dynamic environments, which is crucial for developing intelligent autonomous systems.

The core contribution lies in the development of a data-driven model that learns multimodal distributions of human actions using a Conditional Variational Autoencoder (CVAE) from a dataset of human-human interactions. This approach benefits from recent advancements in sequence-to-sequence learning, allowing the model to capture not only the immediate response of humans to robot actions but also the underlying probabilistic structure across multiple, distinct possible futures. The CVAE framework enables the generation of diverse action sequences, wherein a discrete latent variable captures high-level behavioral modes, and an autoregressive RNN decoder provides a nuanced modeling of transitions within each mode.

Setting the model in the context of a pairwise traffic weaving scenario, the authors demonstrate the model's ability to predict human driver actions conditioned on historical interaction data and projected robot paths. The multimodal nature of these predictions, validated by a human-in-the-loop simulator environment, underscores the adaptability of the model to inherently uncertain environments.

The proposed system for robot policy construction employs a model-based approach where the robot's decision-making is guided by a fixed-horizon optimization problem. This problem considers potential human reactions to candidate robot action sequences over short time intervals, leveraging parallelized sampling methods to efficiently evaluate a vast space of possibilities on commodity GPU hardware.

Empirical results, derived from over a thousand human-human interaction trials within a driving simulator, highlight the robustness and efficiency of the proposed planning approach. The paper reports that the model is able to generate up to 100,000 human-future simulations within a 0.3-second real-time window, showcasing its capacity to perform exhaustive search over action sequences. Such performance is achieved through hierarchical sampling techniques, concentrating computational resources on the most promising candidate paths evaluated under the defined planning horizon costs.

This work provides compelling insights into the integration of probabilistic models with real-time robotic decision-making processes, suggesting broader implications in the field of autonomous systems operating in complex, interactive scenarios. By decoupling action prediction from policy construction, this framework offers interpretability in robot decision-making, facilitating future adaptability to scenarios with more intricate human-robot dynamics.

The practical implications of this research extend to the ongoing development of autonomous vehicles, where systems must adeptly navigate interactions with human drivers under uncertain conditions. Although the data-intensive nature of the work necessitates substantial data collection efforts, such as those from industrial stakeholders, the authors suggest potential for scalability and adaptation across diverse HRI applications.

Looking forward, the integration of this framework with lower-level collision avoidance and higher-level strategic reasoning algorithms is poised to enhance the robustness of autonomous decision architecture. Additionally, enriching human action models with non-verbal cues like gestures or verbalizations could further elevate the realism and safety of human-robot interactions, paving the way for more seamless robotic assimilation into human-centric environments.