Dynamic loco-manipulation calls for effective whole-body control and contact-rich interactions with the object and the environment. Existing learning-based control synthesis relies on training low-level skill policies and explicitly switching with a high-level policy or a hand-designed finite state machine, leading to quasi-static behaviors. In contrast, dynamic tasks such as soccer require the robot to run towards the ball, decelerate to an optimal approach to dribble, and eventually kick a goal—a continuum of smooth motion. To this end, we propose Preferenced Oracle Guided Multi-mode Policies (OGMP) to learn a single policy mastering all the required modes and preferred sequence of transitions to solve uni-object loco-manipulation tasks. We design hybrid automatons as oracles to generate references with continuous dynamics and discrete mode jumps to perform a guided policy optimization through bounded exploration. To enforce learning a desired sequence of mode transitions, we present a task-agnostic preference reward that enhances performance. The proposed approach demonstrates successful loco-manipulation for tasks like soccer and moving boxes omnidirectionally through whole-body control. In soccer, a single policy learns to optimally reach the ball, transition to contact-rich dribbling, and execute successful goal kicks and ball stops. Leveraging the oracle’s abstraction, we solve each loco-manipulation task on robots with varying morphologies, including HECTOR V1, Berkeley Humanoid, Unitree G1, and H1, using the same reward definition and weights.

In this paper, we propose a novel framework for synthesizing a single multimodal control policy capable of generating diverse behaviors (or modes) and emergent inherent transition maneuvers for bipedal locomotion. In our method, we first learn efficient latent encodings for each behavior by training an autoencoder from a dataset of rough reference motions. These latent encodings are used as commands to train a multimodal policy through an adaptive sampling of modes and transitions to ensure consistent performance across different behaviors. We validate the policy's performance in simulation for various distinct locomotion modes such as walking, leaping, jumping on a block, standing idle, and all possible combinations of inter-mode transitions. Finally, we integrate a task-based planner to rapidly generate open-loop mode plans for the trained multimodal policy to solve high-level tasks like reaching a goal position on a challenging terrain. Complex parkour-like motions by smoothly combining the discrete locomotion modes were generated in$\sim 3$min. to traverse tracks with a gap of width 0.45 m, a plateau of height 0.2 m, and a block of height 0.4 m, which are all significant compared to the dimensions of our mini-biped platform.

Linear policies are the simplest class of policies that can achieve stable bipedal walking behaviors in both simulation and hardware. However, a significant challenge in deploying them widely is the difficulty in extending them to more dynamic behaviors like hopping and running. Therefore, in this work, we propose a new class of linear policies in which template models can be embedded. In particular, we show how to embed Spring Loaded Inverted Pendulum (SLIP) model in the policy class and realize perpetual hopping in arbitrary directions. The spring constant of the template model is learned in addition to the remaining parameters of the policy. Given this spring constant, the goal is to realize hopping trajectories using the SLIP model, which are then tracked by the bipedal robot using the linear policy. Continuous hopping with adjustable heading direction was achieved across different terrains in simulation with heading and lateral velocities of up to O.5m/ sec and 0.05m/ sec, respectively. The policy was then transferred to the hardware, and preliminary results (> 10 steps) of hopping were achieved.

In this paper, with a view toward deployment of light-weight control frameworks for bipedal walking robots, we realize end-foot trajectories that are shaped by a single linear feedback policy. We learn this policy via a model-free and a gradient free learning algorithm, Augmented Random Search (ARS), in the two robot platforms Rabbit and Digit. Our contributions are two-fold: a) By using torso and support plane orientation as inputs, we achieve robust walking on slopes of upto 20° in simulation. b) We demonstrate additional behaviors like walking backwards, stepping-in-place, and recovery from external pushes of upto 120 N. The end-result is a robust and a fast feedback control law for bipedal walking on terrains with varying slopes. Towards the end, we also provide preliminary results of hardware transfer to Digit.