Synergistic Action and Style Priors for Learning Legged Locomotion

arXiv:2410.03246 · cs.RO, cs.AI · Submitted 2024-10-04 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: We're looking at the paper: "Synergistic Action and Style Priors for Learning Legged Locomotion".

Dev: Here's the gist of the paper. The gist Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with their environment, but resulting solutions for locomotion are often brittle and unnatural, which this paper addresses by proposing a latent action space representation learned from expert demonstrations as an inductive bias for direct torque control.

Rosa: Latent Action Priors The core proposal is to learn a representation of the expert’s actions that is subsequently used as an action prior in DRL, which allows the policy to directly leverage knowledge contained in the expert’s actions and facilitates more efficient exploration.

Dev: This prior captures correlations between actions, thereby compressing the action space into an informed, low-dimensional manifold.

Rosa: The latent action priors can be used with imitation learning methods such as style rewards for improved expert imitation.

Paper summary: Rosa: So we've heard that the paper "Synergistic Action and Style Priors for Learning Legged Locomotion" proposes using a latent action space learned from expert demonstrations as an inductive bias to help Deep Reinforcement Learning agents learn locomotion, particularly for direct torque control.

Dev: Essentially, they argue that the unrestricted nature of standard DRL makes solutions brittle, so they want to give the policy some prior knowledge by capturing correlations in actions from experts.

Taro: The main claim is that this latent action representation is especially useful because many natural movements can be described in a lower-dimensional space, and learning from expert data helps compress the action space into an informed manifold.

Rosa: They show that using these priors leads to improved sample efficiency and higher rewards, even achieving performance above the expert level on transfer tasks.

Dev: They also demonstrated that adding style rewards for imitation further improves the agent's ability to match the expert's behavior closely during training.

Taro: The paper highlights several things about their setup, like they use a standard nonlinear autoencoder for the latent space and they enforce constraints on those latent action values to stay between negative one and one.

Rosa: And their reward function is a combination of task rewards weighted at zero point six seven and style rewards weighted at zero point three three, where the style reward encourages matching the expert's pose at each time step.

Dev: They used a specific dataset to train their priors, showing that even with limited and undiverse data, like from a single gait cycle for some agents, they could still capture significant variance.

Taro: The results on their benchmarks show that this method improves performance across different environments, and the improvements are particularly visible in joint-level torque control tasks.

Rosa: It seems they are really focusing on how this technique helps with direct torque control policies where standard methods often struggle to find robust solutions.

Dev: So the paper is essentially suggesting that learning from expert demonstrations can be made more efficient and effective by explicitly modeling the underlying structure of those actions through a latent space.

Conclusion: Rosa: So, looking at "Synergistic Action and Style Priors for Learning Legged Locomotion," the main point is using learned latent action priors from expert demonstrations to give DRL policies a better starting point for learning locomotion.

Dev: The authors are showing that this prior significantly boosts sample efficiency and improves the quality of the resulting policy, especially when you’re dealing with direct torque control.

Taro: It’s not just about getting a good policy; it's about showing that this combination of latent action priors and style rewards is a powerful way to push imitation learning further.

Rosa: The implication for us is that we can get better performance on robotics tasks without needing massive, perfectly diverse datasets right from the start.

Dev: The authors are demonstrating that their parameters are relatively robust compared to other methods that usually require a lot of careful tuning of hyperparameters.

Taro: They also show this works well even when learning joint position control, so the applicability is broader than just torque control.

Rosa: So, in short, it’s about finding an efficient way to leverage expert knowledge—specifically through latent action priors—to make robot locomotion learning more practical and effective.

Oliver Hausdorfer, Alexander von Rohr, Eric Lefort, Angela P. Schoellig

Technical University of Munich

cs.RO, cs.AI

Submitted: 2024-10-04

Updated: 2026-10-20

Comments: Humanoids 2026

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 86/100

The gist: The gist Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with their environment, but resulting solutions for locomotion are often brittle and

Key concepts

Latent Action Priors: This concept involves using a nonlinear autoencoder to compress the high-dimensional expert actions into a lower-dimensional latent space. This learned representation acts as a prior, guiding the DRL policy to explore more efficiently by focusing on relevant action correlations rather than random exploration.
Style Rewards: Style rewards are used during imitation learning to encourage the agent's output to match the expert's pose at each time step. This reward function specifically pushes the learned policy toward replicating the expert's observed behavior, leading to closer behavioral replication.
Torque Control Inductive Bias: This refers to introducing prior knowledge into a learning algorithm, which is usually difficult in model-free DRL for direct joint torque control. The paper uses latent action priors as this bias, leveraging expert data to make the learning process more stable and effective for complex physical control.
Reward Structure: The total reward combines task completion ($r_{task}$) and style matching ($r_{style}$). Task reward guides the agent toward locomotion goals, while the style reward ensures that the learned actions closely resemble those of a human expert, optimizing both performance and imitation.

Terminology

Deep Reinforcement Learning — 1× in this paper · explained in 3 episodes
Deep Reinforcement Learning (DRL) — 1× in this paper · explained in 2 episodes
Inductive Bias — 1× in this paper · explained in 3 episodes
Reinforcement Learning — 1× in this paper · explained in 4 episodes

Summary

The gist Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with their environment, but resulting solutions for locomotion are often brittle and unnatural, which this paper addresses by proposing a latent action space representation learned from expert demonstrations as an inductive bias for direct torque control.

Latent Action Priors

The core proposal is to learn a representation of the expert’s actions that is subsequently used as an action prior in DRL, which allows the policy to directly leverage knowledge contained in the expert’s actions and facilitates more efficient exploration. This prior captures correlations between actions, thereby compressing the action space into an informed, low-dimensional manifold. The latent action priors can be used with imitation learning methods such as style rewards for improved expert imitation.

Methodology

The method involves several key components:

Latent Action Priors: A standard nonlinear autoencoder is used to extract this latent space from the actions, and an additional loss term encourages the latent action values to stay in [-1, 1]. The loss function is defined as L (a,aˆ,al) = a−aˆ2 + Lnorm(al), where Lnorm(al) is a specific term designed for this purpose.
Policy Training: The agent is trained using the on-policy algorithm PPO [34], and the policy receives a phase variable φ as input to the policy as required for the style reward.
Reward Structure: The full reward reads r = wtaskrtask +wstylerstyle, where wtask = 0.67 and wstyle = 0.33 are scalar weighting factors with the values from Qian et al. [23]. The style reward component is adopted as rstyle = exp - sum j q j exp −q j2 / 2!, which encourages the agent to match the pose of the expert at each time step.

Results and Performance

The results show that actions from the expert demonstrations can indeed be described using a low number of latent actions, with a latent space size of half the full action space being sufficient to explain more than 97% of the variance across environments. The latent action priors significantly improve the sample efficiency (learning speed) and maximum achieved reward, suggesting that the action priors are a suitable subspace for learning locomotion. Combining style rewards with latent actions improves the results for all agents except Humanoid. Furthermore, adding the style reward further improves performance and reduces variance between seeds in transfer tasks. The combination of a latent action space and style rewards seems powerful for maximally achieved reward and expert imitation.

Discussion

Latent action priors offer a beneficial prior for learning locomotion at the joint torque level, which so far has been difficult due to the unrestricted nature of model-free DRL frameworks. Latent action priors learned from expert demonstrations help to imitate expert behavior with style rewards. The method shows the greatest benefits when learning direct torque control policies. The robustness of our approach’s parameters is a significant advantage compared to other methods that require extensive hyperparameter tuning. However, the dimensionality reduction in the latent action space is not a major driver for performance but rather information about the correlations of the actions which potentially simplifies exploration. The method is suited for learning from such data, as obtaining more diverse expert data required for other imitation learning approaches can be costly in robotics.

Limitations and Future Work

One limitation is that we implemented latent action priors from data stemming from one gait type. Future work can investigate how demonstrations for different gait types can be incorporated in latent action priors, for instance, by conditioning the prior on the desired gait or target velocity. Another limitation is that we implemented latent action priors from data stemming from one gait type. Future work can investigate how demonstrations for different gait types can be incorporated in latent action priors, for instance, by conditioning the prior on the desired gait or target velocity. The paper suggests doing that by conditioning the prior on the desired gait or target velocity. The method is not restricted to learning in torque control, as we also observe improved performance over unbiased PPO in joint-level position control. Future work can investigate how observations from readily available video data can be retargeted to the desired robot platform, and leveraged for our approach. The paper notes that our method is not restricted to learning in torque control, as we also observe improved performance over unbiased PPO in joint-level position control. Future work can investigate how observations from readily available video data can be retargeted to the desired robot platform, and leveraged for our approach. The paper notes that our method is not restricted to learning in torque control, as we also observe improved performance over unbiased PPO in joint-level position control. Future work can investigate how observations from readily available video data can be retargeted to the desired robot platform, and leveraged for our approach. The paper notes that our method is not restricted to learning in torque control, as we also observe improved performance over unbiased PPO in joint-level position control. Future work can investigate how observations from readily available video data can be retargeted to the desired robot platform, and leveraged for our approach. The paper notes that our method is not restricted to learning in torque control, as we also observe improved performance over unbiased PPO in joint-level position control. Future work can investigate how observations from readily available video data can be retargeted to the desired robot platform, and leveraged for our approach. The paper notes that our method is not restricted to learning in torque control, as we also observe improved performance over unbiased PPO in joint-level position control. Future work can investigate how observations from readily available video data can be retargeted to the desired robot platform, and leveraged for our approach. The paper notes that our method is not restricted to learning in torque control, as we also observe improved performance over unbiased PPO in joint-level position control. Future work can investigate how observations from readily available video data can be retargeted to the desired robot platform, and leveraged for our approach. The paper notes that our method is not restricted to learning in torque control, as we also observe improved performance over unbiased PPO in joint-level position control. Future work can investigate how observations from readily available video data can be retargeted to the desired robot platform, and leveraged for our approach. The paper notes that our method is not restricted to learning in torque control, as we also observe improved performance over unbiased PPO in joint-level position control. Future work can investigate how observations from readily available video data can be retargeted to the desired robot platform, and leveraged for our approach. The paper notes that our method is not restricted to learning in torque control, as we also observe improved performance over unbiased PPO in joint-level position control. Future work can investigate how observations from readily available video data can be retargeted to the desired robot platform, and leveraged for our approach.

Improvements for AI systems

Improved sample efficiency for learning direct torque control policies by utilizing latent action priors learned from expert demonstrations, as noted in latent action priors significantly improve the sample efficiency (learning speed).
Enhanced performance on transfer tasks by leveraging latent action priors combined with style rewards, which substantially improve performance on transfer tasks.
Closer replication of expert behavior through style rewards and latent action priors, leading to a gait that is visually closer to the expert demonstration for Unitree A1 and achieving maximally achieved reward.
Robustness towards reward tuning challenges by showing that the combination of latent action space and style rewards provides a robust way to improve imitation of the expert across different weighting factors.
Capability for complex, coupled tasks, as evidenced by achieving a non-zero success rate in environments where two Unitrees A1s jointly have to solve the task of maneuvering a rod to a target location.

Abstract

Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unrestricted nature of the learning algorithms, the resulting solutions are often brittle and appear unnatural. This is especially true for learning direct joint-level torque control, as inductive biases are difficult to integrate into the learning process. We propose an inductive bias for learning locomotion that is especially useful for torque control: latent actions learned from a small dataset of expert demonstrations. This prior allows the policy to directly leverage knowledge contained in the expert's actions and facilitates more efficient exploration. We observe that the agent is not restricted to the reward levels of the demonstration, and performance in transfer tasks is improved significantly. Latent action priors combined with style rewards for imitation lead to a closer replication of the expert's behavior. Videos are available at https://sites.google.com/view/latent-action-priors.