People employ expressive behaviors to effectively communicate and coordinate their actions with others, such as nodding to acknowledge a person glancing at them or saying "excuse me" to pass people in a busy corridor. We would like robots to also demonstrate expressive behaviors in human-robot interaction. Prior work proposes rule-based methods that struggle to scale to new communication modalities or social situations, while data-driven methods require specialized datasets for each social situation the robot is used in. We propose to leverage the rich social context available from large language models (LLMs) and their ability to generate motion based on instructions or user preferences, to generate expressive robot motion that is adaptable and composable, building upon each other. Our approach utilizes few-shot chain-of-thought prompting to translate human language instructions into parametrized control code using the robot's available and learned skills. Through user studies and simulation experiments, we demonstrate that our approach produces behaviors that users found to be competent and easy to understand. Supplementary material can be found at https://generative-expressive-motion.github.io/.

We aim to tackle the problem of expressive behavior generation that is both adaptive to user feedback and composable so that more complex behaviors can build on simpler behaviors. Formally, we define being expressive as the distance between some expert expressive trajectory that could be generated by an animator (or demonstrated) \( \tau_{\text{expert}} \) and a robot trajectory \( \tau \). \( \text{dist}(\tau, \tau_{\text{expert}}) \) can be any desirable distance metric between the two trajectories, e.g., dynamic time warping (DTW). GenEM aims to minimize this distance \( d^* = \min \text{dist}(\tau,\tau_{\text{expert}}) \).

Our approach uses several LLMs in a modular fashion so that each LLM agent plays a distinct role. GenEM takes user language instructions \( l_{in} \in L \) as input and outputs a robot policy \( \pi_\theta \), which is in the form of a parameterized code. Human iterative feedback \( f_i \in L \) can be used to update the policy \( \pi_\theta \). The policy parameters get updated one step at a time given the feedback \( f_i \), where \( i \in \{1,\dots, K\} \). The policy can be instantiated from some initial state \( s_0 \in S \) to produce trajectories \( \tau = \{s_0, a_0, \dots, a_{N-1},s_N\} \) or instantiations of expressive robot behavior.

We conducted two user studies to assess whether our approach, GenEM, can be used to generate expressive behaviors that are perceivable by people. We generated two versions of behaviors: GenEM, and GenEM with iterative Feedback (or GenEM++). In both studies, all comparisons were made against behaviors designed by a professional animator and implemented by a software developer, which we term the oracle animator. In the first study, our goal was to assess whether behaviors that are generated using GenEM and GenEM++ would be perceived similarly to the behaviors created using the oracle animator. In the second study, we attempted to generate behaviors using GenEM and GenEM++ that were similar to the behaviors created using the oracle animator. Both studies aim to demonstrate that our approach is adaptable to human feedback.

We sampled the same prompts in the first user study five times per behavior using API for a simulated Spot robot. Overall, the success rates hint at the generality of our approach to differing robot embodiments.