Venue: CoRL 2023 Conference

Reviews:

final_score: rating: Weak Reject: I recommend rejecting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary_of_paper: This paper addresses the quadruped locomotion control that enables continual jumping in which each hop length can be adaptively changed. It forms a hierarchical structure. The upper layer is a centroidal motion controller acquired through reinforcement learning, which outputs the swing-foot residual, the phase of the gait, and the desired velocity of the base body. In the lower layer, the swing leg controller solves the inverse kinematics, while the stance leg controller determines the desired ground reaction forces. Although the latter is formulated in general as a quadratic programming under inequality constraints, the authors omit the constraints to compute the forces in a closed form, and then clip the forces when they violate the constraints. The simplified computation, the latter runs five times as frequent as the centroidal controller, which runs every 10 ms. The controller enables wider distances of hops than an end-to-end reinforcement-learning-based controller.
review: “Continual jumping” might be better than “continuous jumping.”

While the presented demonstrations are impressive, it is not very clear for the reviewer why the proposed technique enables wider-distance jumping compared with other end-to-end approaches. Careful analyses and discussions are expected.

As far as the reviewer knows, the technique to replace the quadratic programming under inequality constraints with a combination of unconstrained quadratic programming and postprocessing is often employed (see Hyon [*], for example), and is often criticized as it does not provide the strict optimum solution. Although the reviewer does not criticize it, at least he/she does not think it is the original contribution by the authors.

[*] Sang-Ho Hyon, Full-body compliant human-humanoid interaction: Balancing in the presence of unknown external forces, IEEE Transactions on Robotics, 23(5):884-898, 2007.

robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are not well addressed
summary_of_recommendation: The paper would be substantially improved if the original contribution and the lesson-to-learn of this work are clarified.
review_title: null

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary_of_paper: In this work, the authors propose learning a centroidal policy to perform blind continuous jumping with adaptive distances. The main innovations compared to prior systems are (1) allowing learning a swing leg residual for better performance and (2) reformulating the stance leg optimizer to accelerate training. The overall system is shown to jump further than prior methods.
review: Strengths:
- The reformulated stance leg control is great. I believe that the slow speed of solving this QP was a bottleneck for many works that use a hierarchical architecture [22, 23, 30, 32, 33, 35]. Accelerating it addresses an outstanding issue in this paradigm and will be useful for future works.
- Thanks to this, the 20 minutes of training time for CAJun is much faster than the prior works, both in hierarchical methods and in learned jumping [19, 3, 30].
- Comparison with prior published E2E works in Table 1 supports the work's advanced jumping performance.

Weaknesses:
- The scope of the paper's task of (continuous blind jumping) is a bit narrow. It seems like the paper's architectural improvements (swing leg residual and clipped QP training) could be extended to learning all quadrupedal gaits, which would broaden its implications. 
- I noticed in Figure 3 of [17] and the corresponding subsection titled "Agile forward leap" describe crossing a gap of width 60cm using end-to-end pronking. Is that evaluation different from the others in Table 1?
- Some incomplete reporting on the ablation study (see below)
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: This paper proposes a few nice improvements in the centroidal policy training and demonstrates a new capability (longest jump distance). The paper can be made stronger by (a) more thorough quantitative evaluation of the ablations (section 6.6), and (b) a slight expansion of scope in the demonstrated behaviors or architectural analysis.
review_title: null

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary_of_paper: The submission presents a pipeline for learning a jumping controller for a Unitree Go1 quadruped. The approach uses RL for sim2real of a higher-level controller, that outputs des. CoM velocities, step timing and residuals of the foot positions with respect to a given nominal trajectory. The des. joint torques are computed using two separate controllers: The Swing-leg controller uses IK and a PD-controller to track the desired foot trajectory; the stance-leg controller first uses a PD-controller to compute desired CoM accelerations based on the des. velocities, and then solves a QP to find des. ground reaction forces and foot Jacobian to obtain the stance leg torques. As solving the constrained QP within RL would become a bottleneck, force limits and friction cone constraints are ignored to obtain an unconstrained GP that can be solved using least-squares, and clipping/scaling the resulting forces. Two jumping gaits are learned that can jump variable distances specified as context.
review: Strengths
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- By presenting a working pipeline for learning jumping gaits on a widely available quadruped, the paper is interesting and relevant to parts of the audience.

- The paper is well written, and the approach is quite clear.

- The approach is sound. We can argue that the RL policy should learn to output desired endeffector/CoM trajectories, such that the least-squares solution satisfies the constraints sufficiently well to avoid detrimental effects from the clipping/scaling.

Weaknesses
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- There is not much novelty, because the pipeline is rather standard. The main contribution is to show that the pipeline works in this use case, not so much in the pipeline itself.

- The particular projection is not well motivated. In what sense are the projected ground forces closes to least-squares solutions?   

robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The submission presents a pipeline for learning variable-distance jumping gaits for a lightweight quadruped and demonstrates its applicable. The novelty of the pipeline is a bit unclear, but I do think that the results are of some interest.
review_title: null

Meta-review: The paper proposes a learning policy method to perform blind continuous jumping with adaptive distances. The method has novelty in allowing learning a swing leg residual and accelerated training. For the camera-ready please include the changes as per reviewers' suggestions (such as additional experiments and Additional experiments).
Decision: Accept (Poster)