Sampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learning

Sim-to-real discrepancies hinder learning-based policies from achieving high-precision tasks in the real world. While Domain Randomization (DR) is commonly used to bridge this gap, it often relies on heuristics and can lead to overly conservative policies with degrading performance when not properly tuned. System Identification (Sys-ID) offers a targeted approach, but standard techniques rely on differentiable dynamics and/or direct torque measurement, assumptions that rarely hold for contact-rich legged systems. To this end, we present SPI-Active (Sampling-based Parameter Identification with Active Exploration), a two-stage framework that estimates physical parameters of legged robots to minimize the sim-to-real gap. SPI-Active robustly identifies key physical parameters through massive parallel sampling, minimizing state prediction errors between simulated and real-world trajectories. To further improve the informativeness of collected data, we introduce an active exploration strategy that maximizes the Fisher Information of the collected real-world trajectories via optimizing the input commands of an exploration policy. This targeted exploration leads to accurate identification and better generalization across diverse tasks. Experiments demonstrate that SPI-Active enables precise sim-to-real transfer of learned policies to the real world, outperforming baselines by 42-63% in various locomotion tasks.

Dynamic Bipedal Loco-manipulation using Oracle Guided Multi-mode Policies with Mode-transition Preference

Loco-manipulation calls for effective whole-body control and contact-rich interactions with the object and the environment. Existing learning-based control frameworks rely on task-specific engineered rewards, training a set of low-level skill policies and explicitly switching between them with a high-level policy or FSM, leading to quasi-static and fragile transitions between skills. In contrast, for solving highly dynamic tasks such as soccer, the robot should run towards the ball, decelerating into an optimal approach configuration to seamlessly switch to dribbling and eventually score a goal - a continuum of smooth motion. To this end, we propose to learn a single Oracle Guided Multi-mode Policy (OGMP) for mastering all the required modes and transition maneuvers to solve uni-object bipedal loco-manipulation tasks. Specifically, we design a multi-mode oracle as a closed loop state-reference generator, viewing it as a hybrid automaton with continuous reference generating dynamics and discrete mode jumps. Given such an oracle, we then train an OGMP through bounded exploration around the generated reference. Furthermore, to enforce the policy to learn the desired sequence of mode transitions, we present a novel task-agnostic mode-switching preference reward that enhances performance. The proposed approach results in successful dynamic loco-manipulation in omnidirectional soccer and box-moving tasks with a 16-DoF bipedal robot HECTOR. Supplementary video results are available at https://www.youtube.com/watch?v=gfDaRqobheg

OGMP: Oracle Guided Multimodal Policies for Agile and Versatile Robot Control

Amidst task-specific learning-based control synthesis frameworks that achieve impressive empirical results, a unified framework that systematically constructs an optimal policy for sufficiently solving a general notion of a task is absent. Hence, we propose a theoretical framework for a task-centered control synthesis leveraging two critical ideas: 1) oracle-guided policy optimization for the non-limiting integration of sub-optimal task-based priors to guide the policy optimization and 2) task-vital multimodality to break down solving a task into executing a sequence of behavioral modes. The proposed approach results in highly agile parkour and diving on a 16-DoF dynamic bipedal robot. The obtained policy advances indefinitely on a track, performing leaps and jumps of varying lengths and heights for the parkour task. Corresponding to the dive task, the policy demonstrates front, back, and side flips from various initial heights. Finally, we introduce a novel latent mode space reachability analysis to study our policies' versatility and generalization by computing a feasible mode set function through which we certify a set of failure-free modes for our policy to perform at any given state.