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.
  • 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:
  • 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

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:
  • 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:
  • 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

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.
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