# Project List

## Submission Instructions

1. Template: Use the PKU-IAI Technical Report (TR) Template available on the LaTeX Template page. Do not use the Course Project and Essay Template; it is different from the TR template.

2. Initial Submission and Feedback:

• Usually two weeks before the final submission.
• Submit your paper to OpenReview for review.
• Receive feedback from the instructor and TAs.
• Add the PKU-IAI technical report ID to the LaTeX template.
• Update your paper based on the feedback.
3. Final Submission:

• Also upload code, models, and all steps needed to reproduce your results.
• Use OpenReview, GitHub, OSF, GDrive, or WandB for uploading supplementary materials.
• Complete all steps to receive full credit.

## Project List

### Affordance and Functionality

#### 1. Replicate GPNN Paper Results

• Difficulty:
• Objective: Replicate findings from the GPNN paper.
• Evaluation: Match paper’s tables and figures; discuss discrepancies.

#### 2. Compare Neural Parts and Cuboid Shape Abstraction on PartNet

• Difficulty:
• Objective: Implement and compare Neural Parts and Cuboid Shape Abstraction on PartNet’s chair category.
• Evaluation: Use IoU, Precision, and Recall for qualitative and quantitative comparison.

#### 3. Learn the Concept of a Daily Object (e.g., a Cup)

• Difficulty:
• Objective: Develop a machine learning model to understand and represent the concept of a daily object like a cup.
• Define the key components of the object’s concept.
• Formulate the problem and method.
• Collect necessary training data.
• Design evaluation metrics.
• Evaluation: Use qualitative and quantitative metrics to prove the model’s effectiveness in learning the object’s concept.

### Intuitive Physics

#### 1. Model Visually Grounded VoEs with SOTA Algorithms

• Difficulty:
• Objective: Use SOTA computer vision algorithms to model Visually Grounded Violations of Expectation (VoEs).
• Evaluation: Accurately measure the model’s “surprise” metric when a VoE is presented.

#### 2. Probabilistic Model for Water-Pouring Task

• Difficulty:
• Objective: Create a probabilistic model to solve the water-pouring task.
• Reference Models: Tom Griffiths and Josh Tenenbaum’s groups.
• Evaluation: Predict the glass angle for water pouring; compare with human performance and reference models.

### Causality

#### Model-based RL for Causal Transfer in OpenLock

• Difficulty:
• Background:
• Question the “Reward is all you need” paradigm in RL.
• Focus on causal transfer in the OpenLock task, a virtual escaping game.
• Model-free RL has limitations in understanding abstract causal structures.
• Objective:
• Design a model-based RL method for OpenLock.
• Address the task’s focus on understanding abstract causal structures and utilizing implicit meta-rules.
• Clearly state your model construction.
• Compare with model-free RL.
• Optional: Handle probabilistic OpenLock scenarios.
• Evaluation:
• Compare your model-based RL with model-free methods.
• Optional: Include results for probabilistic OpenLock scenarios.

### Tool, Mirroring, and Imitation

#### Virtual Tool Game with Compositional Concepts

• Difficulty:
• Background:
• Objective:
• Design a new scenario leveraging compositional concepts (e.g., Bridge + Catapult).
• Propose a model that can solve this new compositional problem while learning individual concepts.
• Reproduce baselines from the referred paper.
• Create a new scenario involving at least two compositional concepts.
• Develop a model to solve the new problem.
• Evaluation:
• Compare your model’s performance with baseline models.
• Validate its ability to understand and apply compositional concepts.

### Communication

#### 1. Cooperation through Nonverbal Cues

• Difficulty:
• Background:
• Study shows chimpanzees use nonverbal cues for cooperation tasks.
• Objective:
• Build a simulated scenario to computationally reproduce these experimental results.
• Include nonverbal communication cues like gaze and pointing.
• Develop a policy for nonverbal communication under a shared goal.
• Evaluation:
• Validate the model’s ability to use nonverbal cues effectively in a cooperative setting.

#### 2. Emergent Languages in Multi-Agent Systems

• Difficulty:
• Background:
• Multi-agent systems can develop emergent languages.
• Objective:
• Design a task and environment for agents to develop an emergent language.
• Use the EGG toolkit for training.
• Develop evaluation metrics and report results.
• Evaluation:
• Assess whether agents successfully solve the task through emergent communication.

#### 3. Rational Speech Acts (RSA) Model

• Difficulty:
• Background:
• Familiarize yourself with key papers on the Rational Speech Acts (RSA) Model:
• Objective:
• Implement a literal and a pragmatic agent based on the RSA model.
• Use the TUNA Corpus for experiments.
• Choose a category (people or furniture) from the singular portion for your experiments.
• Evaluation:
• Use mean accuracy and multiset Dice as formulated in Eqn. (6) to evaluate the agents.

### Intentionality

#### Multi-agent Activity Parsing and Prediction on LEMMA

• Difficulty:
• Background:
• The focus is on activity parsing and prediction in multi-agent scenarios using LEMMA.
• Objective:
• Use grammar parsing or planning methods in a neural-symbolic way.
• Address challenges like multi-agent activity representation and symbolic plan structures.
• Explore evaluation methods beyond future activity prediction.
• Evaluation:
• Assess the model’s ability to parse and predict multi-agent activities.

### Animacy

#### Generate Animate and Inanimate Dot Motion

• Difficulty:
• Background:
• Explore the unified problem of animate and inanimate motion.
• Objective:
• Generate diverse animate and inanimate dot motion stimuli.
• Formulate the problem for both synthesis and discriminative tasks.
• Perform a human study for model verification.
• Evaluation:
• Assess the model’s classification accuracy on provided stimuli.

### Theory of Mind (ToM)

#### 1. Build a Mini Theory of Mind System

• Difficulty:
• Background:
• Theory of Mind involves understanding mental states like desires, beliefs, and intents.
• Objective:
• Build a system that models these mental states in real or simulated scenarios.
• Do NOT use open-sourced projects.
• Include modules for desire, belief, and intent.
• Implement inverse mental inference and forward planning processes.
• Evaluation:
• Validate the system’s capability in modeling interactions based on ToM.

#### 2. Hanabi Challenge with Official Environment

• Difficulty:
• Background:
• The Hanabi challenge focuses on cooperative multi-agent systems.
• Objective:
• Build an AI agent to tackle the Hanabi challenge.
• Evaluation:
• Assess the agent’s performance in the Hanabi environment.

#### 3. Action Understanding as Inverse Planning

• Difficulty:
• Background:
• Familiarize yourself with the Food truck paper and understand the Bayesian inverse planning framework.
• Objective:
• Implement the Bayesian inverse planning model to infer agents’ goals and beliefs.
• Validate the model through psychophysical experiments using animated stimuli.
• Implement the Bayesian inverse planning model based on Markov decision problems (MDPs).
• Create animated stimuli of agents moving in simple mazes as described in the paper.
• Conduct experiments to measure online goal inferences, retrospective goal inferences, and prediction of future actions.
• Evaluation:
• Assess the model’s ability to accurately infer agents’ goals and beliefs.

### Abstract Reasoning

#### 1. Probabilistic PDDL Solver for Block Stacking

• Difficulty:
• Background:
• Explore probabilistic PDDL solvers in the context of block stacking.
• Objective:
• Implement the solver and reproduce block stacking experiments.
• Evaluation:
• Validate the solver’s performance in block stacking tasks.

#### 2. Minimal Differentiable Engine for Convex Optimization

• Difficulty:
• Background:
• The focus is on supporting implicit convex optimization and differentiation.
• Objective:
• Design a minimal differentiable engine.
• Implement the engine and validate its capabilities.
• Evaluation:
• Assess the engine’s performance in optimization tasks.

#### 3. Implement DreamCoder and Reproduce Experiments

• Difficulty:
• Background:
• DreamCoder focuses on program synthesis and learning-to-learn.
• Objective:
• Implement DreamCoder in Python/PyTorch and reproduce at least one experiment.
• Follow the DreamCoder framework for program synthesis.
• Choose an experiment to reproduce.
• Evaluation:
• Validate the implementation by comparing your results with the original experiment.

### Utility

#### Learning Human Utility for Object Arrangement

• Difficulty:
• Background:
• The agent aims to infer human utility for arranging objects according to different user preferences.
• Objective:
• Develop a utility function to represent common norms and individual preferences.
• Use a simulation for the environment.
• Training: Utilize a set of arranged examples with user IDs.
• Testing: Use examples provided by a specific user.
• Input:
• A simulation environment.
• Training: A set of arranged examples with user IDs.
• Testing: Examples provided by a specific user.
• Output:
• A utility function representing common norms and human preferences.
• A policy for object arrangement based on the learned utility function.
• Evaluation:
• Validate the learned utility function and policy against user preferences.
• Reference:

### XAI and Teaming

#### Watch-And-Help Benchmark in Virtual Home Environment

• Difficulty:
• Background:
• The Watch-And-Help benchmark focuses on human-robot teaming with goals and intents.
• Objective:
• Develop algorithms to solve the human-robot teaming problem considering goals and intents.