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: The authors present a framework for learning manipulation skills, which they refer to as mechanisms, and are composed of sequences of defined basis manipulation operations. The problem of manipulation is looked at as a STRIPS planning problem. As a result, even a single user demonstration of a task can be decomposed into parametrised manipulation operations and then repeated. The author present results from their method in simulation on a series of manipulation tasks of varying complexity.
review: The paper presents a problem that is relevant for the robot learning community - being sample efficient and being able to generalise well in long-horizon tasks. 

Strengths:
* the authors choose to look at learning from demonstration as a hybrid system which ultimately means representing some of the information encoded in the user demonstrations as symbols. As a result they can utilise off-the-shelf proven planning algorithms and planning languages which in turn allows them to be very efficient and to generalise to novel skills, even non-demonstrated ones.

Weaknesses:
* the method is heavily dependant on having state information from the simulator about contacts, in order to be able to decompose a single demonstration into its subparts.
* the paper is generally hard to read and introduces a lot of it's own naming and terms that are not clearly explained and examplified - i.e. manipulation skills are mechanisms, briefly-dynamic, learned specialised samplers (I'm still not sure what are the samplers even after reading the paper a few times.)
* all experiments are solely done in simulation, partly because of the method is too dependent on having full state observability. It is unclear whether and how much the method would succeed in scenarios where that's not the case - i.e. the real world with physical robots, noisy sensors and very-hard-to-estimate contacts purely from vision.
robotics_focus: Relevant but unlikely to deploy to hardware in near future
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: I recommend weak reject but would be willing to change my recommendation during the rebuttal period.
review_title: null

final_score: rating: Strong Accept: I recommend accepting the paper and will argue for my recommendation even if other reviewers hold a different opinion.
review_content: summary_of_paper: The submitted paper presents a new framework for learning reusable manipulation "mechanisms". These mechanisms can be learnt from a single demonstration and then additional self-play. A mechanism learns the sequence of contact mode changes between the robot, objects and support surfaces. Planning with mechanisms assists with efficiently creating long-horizon plans.
review: Strengths:

- The paper presents a new framework for learning a useful manipulation strategy composed of multiple basis operations (or lower-level skills) that utilises the change in a contact mode graph to understand the transition between basis operations. 
- The idea I believe is unique and is framed in a way such that it is useful for long-horizon task planning where there are also continuous variables (task and motion planning).
- The authors propose a method for learning new mechanisms from a single demonstration and then utilising self-play to improve the generalisation of the mechanism to a known set of objects.
- The paper is generally well written with ideas clearly and logically presented although there are points where refinement could help to avoid confusion. For example, the delineation between skills, tricks, mechanisms, strategies, basis operations etc. could be made clearer and earlier. 

Weaknesses:

- The authors fail to compare their method against any competitive or state-of-the-art baseline. An option here might be a hierarchical RL policy seeing as there are a fixed set of objects and all experiments are completed in simulation. 
- There is mention that this method is able to handle briefly-dynamic tasks, however, there is no proof of this in the main body of the paper and seeing as planning and execution is completed in simulation it is difficult to see how this would remain true if transferred to real-world environments. 
- The limitations of devising a manipulation strategy as a mechanism are not explored nor are the requirements for the demonstration. 
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: The authors have presented a paper that introduces a framework for learning "mechanisms" that are composed of lower-level basis operations. The idea leverages the sequence of contact mode changes in a demonstration to efficiently learn. Overall the work is well presented although is missing some discussion on the limitations of the proposed method and the requirements for the demonstration.
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: This work introduces a novel method for learning reusable manipulation strategies. The core functionality of the proposed approach is the ability to sequence single demonstrations into smaller, core skills that can be sequenced together. The individual skills are then learnt through trial and error and by using self-play. Overall, the paper is well written and clearly discusses both pros and cons of the proposed approach. The evaluation, conducted in simulated environments, is extensive and well done. My only big concern, also acknowledged in the limitations section, is that this method is not straightforwardly generalised to physical systems. Having said that, I find this work as an excellent paper but question its fit for CoRL.
review: ## Strengths and Weaknesses
### Strengths

 - An automated method for learning reusable strategies from limited number of demonstrations
 - An answer to an important question of how to build reusable manipulation skills
 - A well written paper

### Weaknesses
 - Unclear scalability to physical systems
 - Convoluted algorithm that does not necessarily scale to dexterous tasks
robotics_focus: Relevant but unlikely to deploy to hardware in near future
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: Overall, I like this approach but I am not convinced it is a great fit for the CoRL community. Having said that, it seems like a well executed paper and as a result I recommend a weak reject with mild confidence purely on the basis of its fit to the CoRL community.
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 authors present an approach for learning manipulation mechanisms. A mechanism is a reusable sequence of "manipulation primitives" that comes with an individual sampling strategy for each primitive. While the primitives seem to be predefined, the learned manipulation mechanisms can be reused in other learning setups. Individual primitives and mechanisms are parametrized such that they are not limited to single tools but can also be generalized to setups which are different from the single demonstration that was used to learn them.
review: - One major weakness which is also discussed in the limitations section by the authors are the missing real robot experiments.

- I understand that the presented concept is sampling based and can be integrated into sampling-based task planners. I would like to see a clarifying statement on why a comparison to existing approaches is difficult, since in the exerperimental section the presented method has only been compared to a baseline approach.

- As also discussed by the authors uncertainty and imprecise demonstrations are a major issue with the approach. However, I also believe those topics can be tackled by follow-up work. Additionally, the aforementioned difficulties are shared with other planning approaches and are not specific for the presented approach.

- Line 154: they present -> they are present
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I chose my rating mainly because I did not find any major flaws in the paper. I don't think my major concerns and questions about the approach in general have to be tackled in this paper, but can also be follow-up work.
review_title: null

Meta-review: This work presents an approach to learn reusable skills, referred to as "mechanisms". These mechanisms learn sequences of contact mode changes between the robot and objects based on a single virtual demonstration and subsequent self-play. While the evaluations in the manuscript were only performed in simulation, the authors provide real world experiments during the rebuttal period. Unfortunately, the approach still requires an initial virtual demonstration even for the real world experiments, which is not ideal but I it should not be a disqualifying limitation.

The authors generally did a good job in the rebuttal and I believe given the revisions described in the rebuttal that this work can be accepted for CoRL 23.
Decision: Accept (Poster)