Paper: CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning (ICLR-2024)

Reviewer comment (Evaluation):
Missing evaluation on the R2H-Mapper and H2R-Mapper. As the main reward provider, it is important to visualize how well these learned networks can translate between the two different modalities of motion. Do certain correspondences emerge (e.g. robot forward foot mapping to hands)? Are there failure modes that the mapper didn't learn?

Author response:
We add a paragraph in the result section 5.1 to analyze and visualize the result of the learned mapping. Here we summarize our analysis and findings:
- The mappers can accurately reconstruct human and robot poses when the robot actively follows human movement.
- In scenarios where human motion is relatively stationary, human arm movements can be mapped onto the robot's front legs.
- When mapping high-speed human locomotion, the system tends to overlook human arm motion and focuses primarily on leg motion. In such cases, the mapper cannot fully reconstruct the human pose.
- When the variety of human motions is limited, the mappers can overfit to certain poses, leading to suboptimal motion transfer results.

Reviewer comment (Presentation):
Since the mapping between poses $p^h$ and $p^r$ is done locally, showing the reconstructed result together with the ground truth root would be beneficial.

Author response:
We appreciate the reviewer's comments and have taken them into consideration. 

We have uploaded a revised version of the paper to address the suggested changes. 

In particular, we have included the reconstruction error of each pose in Figure 3. These values indicate that when the reconstruction error is low, there is a strong correspondence between human and robot motion, allowing the robot to accurately represent the human's motion. On the other hand, when the error is high, the correspondence between the two domains is weaker, leading the robot to disregard the human's arm motion.

Reviewer comment (Evaluation):
Though the qualitative results provide a number of motion sequences, most of them are locomotion sequences (walking and running) which a Task-only controller should solve relatively easily. The only semantically meaningful sequences are dancing, of which there are only a handful of examples. There is a glimpse of good semantic mapping, but it is not super conclusive. Instead of focusing on dancing, simpler motions like raising hands, reaching for objects, and more complex hand movements could be more suitable for evaluating this system's capabilities. Similar to the previous point about evaluating the mappers, it is not clear how well each part of the human body is mapped.

Author response:
We appreciate your valuable suggestion. We included additional example in the paper Figure 3 and a supplemental video regarding hand motion. Regarding the diversity of locomotion sequences compared to a Task-only controller, please refer to the answer to the previous question for more details.

Reviewer comment (Experiments):
The results on Deepmimic show that an expert-designed mapping may not be plausible and not easy to learn. However, since there is no study on what kind of mapping CrossLoco learned, it is hard to see how CrossLoco is better. Questions remain, such as whether a good mapping exists between bipedal and quadruped robots, whether this task is too ill-posed, or whether we simply have not found a good mapping yet.

Author response:
We contend that our mapping approach is superior because it captures subtle and detailed nuances, such as variations in dancing or gait styles at the same speed, which [Kim et al. 2022] and [Li et al. 2023] fail to capture. Moreover, our concurrent training methodology based on reinforcement learning (RL) enables us to discover physically plausible motions, a feature not consistently present in previous methods. Finally, manually designing an effective, universal retargeting strategy for all types of motions is challenging. In contrast, CrossLoco effectively processes the human motion dataset as a whole to develop a universal strategy.

Reviewer comment (Experiments):
Need to discuss the limitations of the current framework. On what human motion does the matching score the lowest? Which human motion can be best matched? What happens if more or fewer human motion sequences are used for learning the mapping?

Author response:
We discussed several failure cases in section 5.1 paragraph 3. These cases involve human backward walking and swift side steps. In the section, we also have added more analysis on the cases where less diverse human motion sequences are used.

Reviewer comment (Experiments):
Since the mapping between poses $p^h$ and $p^r$ is done locally, showing the reconstructed result together with the ground truth (GT) root would be beneficial.

Author response:
We appreciate the reviewer's comments and have taken them into consideration. 

We have uploaded a revised version of the paper to address the suggested changes. 

In particular, we have included the reconstruction error of each pose in Figure 3. These values indicate that when the reconstruction error is low, there is a strong correspondence between human and robot motion, allowing the robot to accurately represent the human's motion. On the other hand, when the error is high, the correspondence between the two domains is weaker, leading the robot to disregard the human's arm motion.

Reviewer comment (Evaluation):
Providing results on the two Mappers, either a reconstruction metric or visualizations, could really help in understanding the capability of the framework. It would be important to determine whether the algorithm learns some novel mapping.

Author response:
We add a paragraph in the result section 5.1 to analyze and visualize the result of the learned mapping. Here we summarize our analysis and findings:
- The mappers can accurately reconstruct human and robot poses when the robot actively follows human movement.
- In scenarios where human motion is relatively stationary, human arm movements can be mapped onto the robot's front legs.
- When mapping high-speed human locomotion, the system tends to overlook human arm motion and focuses primarily on leg motion. In such cases, the mapper cannot fully reconstruct the human pose.
- When the variety of human motions is limited, the mappers can overfit to certain poses, leading to suboptimal motion transfer results.