Project List

Submission Instructions

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.

Project List

Affordance and Functionality

1. Replicate GPNN Paper Results

• Difficulty:
• Objective: Replicate findings from the GPNN paper. If the data link is expired, you can get them here.
• 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.
• Subtasks:
  • 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.
• Task Details:
  • 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:
  • Explore the Virtual Tool Game and understand the baselines in the referred paper.
• 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.
• Task Details:
  • 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.
• Task Details:
  • 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.
• Task Details:
  • 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:
    • Learning in the Rational Speech Acts Model
    • Knowledge and Implicature: Modeling Language Understanding as Social Cognition
    • Pragmatic Language Interpretation as Probabilistic Inference
• Objective:
  • Implement a literal and a pragmatic agent based on the RSA model.
• Task Details:
  • 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.
• Task Details:
  • 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.
• Task Details:
  • 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.
• Task Details:
  • Do NOT use open-sourced ToM 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.
• Task Details:
  • Use the official environment.
• 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.
• Task Details:
  • 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.
• Task Details:
  • Follow the experiments outlined in the paper by Huang, De-An, et al..
• 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.
• Task Details:
  • Implement the engine and validate its capabilities.
• Evaluation:
  • Assess the engine’s performance in optimization tasks.

3. Implement BPL + Language Model and Reproduce Experiments

• Difficulty:
• Background:
  • Ellis, 2023 introduces a Bayesian reasoning process where a language model first proposes candidate hypotheses expressed in natural language, which are then re-weighed by a prior and a likelihood.
• Objective:
  • Implement the model in Python/PyTorch and reproduce at least one experiment.
• Task Details:
  • Follow the proposed framework.
  • Choose an experiment (number game or logical concepts learning) 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.
• Task Details:
  • 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:
  • Organizing objects by predicting user preferences through collaborative filtering, IJRR 2016
  • Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars, IJCV 2018
  • Example-based Synthesis of 3D Object Arrangements, TOG 2012
  • My House, My Rules: Learning Tidying Preferences with Graph Neural Networks, CoRL 2021
  • B-Pref: Benchmarking Preference-Based Reinforcement Learning, NeurIPS 2021

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.
• Task Details:
  • Reproduce baselines for the Watch-And-Help benchmark.
  • Propose new algorithms considering goals and intents.
• Evaluation:
  • Compare your algorithms with existing baselines.