reviews: 
Comments: The paper proposes a method to generate physical human-scene interactions from language inputs. The method is capable of generating multiple tasks with a single model. 

A large language model (LLM) is used to translate the language prompt (e.g go sit on the chair) into a plan of multiple sequential steps (e.g. go closer to the chair, make contact between hip and seating area in the chair). Each step is defined as the contact between a human joint and an object part. The paper calls this: chain of contact.

The authors derive observation and reward based on the contact pair. A humanoid agent is trained using AMP (Peng et al. 2021) to maximize the sum of rewards for all the steps specified by the LLM. 

To train the humanoid agent, the authors collected a dataset of plans by prompting the LLM in different scenarios. These scenarios require access 3D scene data (i.e. object meshes, scene layout, object parts).
Strengths: The paper is novel, tackles an important problem, provides interesting results, opens the door for exciting future directions, is easy to read, and is technically sound.
- The main novelties of the paper are:

1. Using LLM as a task planner. i.e. using LLM to convert textual prompts into smaller executable steps.
2. Representing all the steps as contact pairs. i.e chain of contact
3. Unifying the design of the observation and reward for all tasks.

- The method is capable of generating multiple interactions with the same model.
Weaknesses: - The motion quality looks worse than previous works like InterPhys, AMP, or NSM. 
- The paper mentions the use of prompt engineering to generate the plan data. It is not entirely clear how was this done. How difficult was this? How sensitive is the model to changes in the prompt? I think this process involves several steps which were not discussed in detail. Thus I think it will be hard to reproduce this part.
- The LLM expects examples of a task plan with each prompt. It is unclear how many examples were needed in total. How were these examples generated? manually?
- The comparison in Table 3 seems weak. I understand the difficulty since the code for InterPhys is unavailable. However, I still have many questions about the comparison. How many test objects were used and how many trails were run? Did you use similar numbers as the ones used by InterPhys Hassan et al 2023? In addition, the performance of AMP seems quite low. InterPhys is an extension of AMP so I expect AMP to perform better than the provided numbers. I suspect the model has not been trained properly.
- The method does not handle interaction with moving objects.

Minor weaknesses\comments for improvements
- There are three recent(possibly concurrent) works that use LLM as an agent planner. It would be helpful to the reader to discuss the differences between the proposed methods and their approaches. 
1. Athanasiou et al."SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation"
2. Brohan et al."RT-2: New model translates vision and language into action"
3. Rocamonde† et al. "VISION-LANGUAGE MODELS ARE ZERO-SHOT REWARD MODELS FOR REINFORCEMENT LEARNING"
- The authors argue for the value of language input. Language is useful for some applications but it is not necessarily a better or more user-friendly interface. For many users, a joystick, brush, and fingers might be more comfortable. Especially in games or digital design.
- The contact pair contains 5 elements. Then it is not a pair. Maybe it is better to be called a contact tuple or a set.
- It would be nice to see an example of when the human plan succeeded but the LLM one. It would be useful to put both plans side by side
Recommendation: 8: accept, good paper
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 3 good