EmoLo: Emotion-Inspired Expressive Locomotion via Single-Policy Reinforcement Learning on Low-Cost Bipedal Robots

Legged robots in human-centered settings should combine reliable locomotion with behavior that is expressive and easy to interpret. This paper presents a style-conditioned reinforcement learning framework for Open Duck Mini V2 that generates emotion-inspired walking behavior through three discrete styles-Happy, Neutral, and Sad- using a single shared policy. The policy is trained in simulation with a two-part objective: locomotion terms are inherited from an open-source baseline, while a compact style objective modulates head-pitch posture and motion activity through bounded rewards and a command-dependent gate. Training incorporates sim-to-real considerations such as sensor noise, delay, external pushes, and motor limits, and the controller is exported to ONNX for onboard inference. We evaluate the method in simulation and on hardware under a controlled forward-walking protocol, focusing on trial completion and head-pitch trajectories. The results show successful locomotion with consistent style-dependent head behavior, demonstrating that emotion-inspired expressive modulation can be integrated into a deployable low-cost bipedal controller without multiple policies. Additional material is available at https://mertcookimg.github.io/emolo