Leslie Kaelbling
Panasonic Professor
Studies robotics, machine learning, and planning under uncertainty.
Leslie Kaelbling's lab at MIT works on planning, control & multi-robot systems and probabilistic & causal inference. 72 papers since 2023, 54 at top venues, mostly ICLR and CoRL. 12 PhD students. 122 alumni. Applications close .
At a glance
- papers, 2023–2026
- 19/ year
- at T1 venues
- 75%
- PhD students
- 12
- MS students
- 0
- alumni listed
- 122
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 1 · collecting
- 15 papers2025 · T1 12
- 23 papers2024 · T1 16
- 30 papers2023 · T1 25
T1 venuesOtherVenues: ICLR, CoRL, RSS, ICML, ICRA
Research focuspaper titles that use each topic's words
- Planning, control & multi-robot systems24 papers
- planning22 papers
- uncertainty3 papers
- Probabilistic & causal inference1 paper
- studies robotics0 papers
Paper list
From the lab homepage.
20264 papers
- RA-LFrom Pixels to Predicates: Learning Symbolic World Models Via Pretrained VLMs (opens in a new tab)
- RA-LOpen-World Task and Motion Planning Via Vision-Language Model Generated Constraints (opens in a new tab)
- RSS WorkshopPROXY: Learning Object-Centric Manipulation Policies from a Single RGB-D Image (opens in a new tab)
- IROS (T1)Which Reconstruction Model Should a Robot Use? Routing Image-to-3D Models for Cost-Aware Robotic Manipulation (opens in a new tab)
202515 papers
- ICML (T1)Agent Workflow Memory (opens in a new tab)
- NeurIPS (T1)Creativity or Brute Force? Using Brainteasers as a Window into the Problem-Solving Abilities of Large Language Models (opens in a new tab)
- RSS (T1)Differentiable GPU-Parallelized Task and Motion Planning (opens in a new tab)
- ICML (T1)Flow-based Domain Randomization for Learning and Sequencing Robotic Skills (opens in a new tab)
- ICRA (T1)Guiding Long-Horizon Task and Motion Planning with Vision Language Models (opens in a new tab)
- AAAI (T1)Infer Human’s Intentions Before Following Natural Language Instructions (opens in a new tab)
- ICRA (T1)KALM: Keypoint Abstraction using Large Models for Object-Relative Imitation Learning (opens in a new tab)
- NeurIPS (T1)Learning Linear Attention in Polynomial Time (opens in a new tab)
- CoRL (T1)LLM-Guided Probabilistic Program Induction for POMDP Model Estim (opens in a new tab)
- ICRA (T1)One-Shot Manipulation Strategy Learning by Making Contact Analogies (opens in a new tab)
- AAAI WorkshopPredicate Invention from Pixels via Pretrained Vision-Language Models (opens in a new tab)
- Designing an Intelligence (George Konidaris Ed.) MIT PressRationally Engineering Rational Robots (opens in a new tab)
202423 papers
- RSS (T1)“Set It Up!”: Functional Object Arrangement with Compositional Generative Models (opens in a new tab)
- WAFRBi-Level Belief Space Search for Compliant Part Mating Under Uncertainty (opens in a new tab)
- CoRL (T1)BLADE: Learning Compositional Behaviors from Demonstration and Language (opens in a new tab)
- ICML (T1)Compositional Generative Modeling: A Single Model is Not All You Need (opens in a new tab)
- IROS (T1)DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability (opens in a new tab)
- NeurIPS Datasets and Benchmarks TrackEmbodied Agent Interface: A Single Line to Evaluate LLMs for Embodied Decision Making (opens in a new tab)
- IROS (T1)Embodied Uncertainty-Aware Object Segmentation (opens in a new tab)
- CogSciFinding Structure in Logographic Writing with Library Learning (opens in a new tab)
- AAAI (T1)Generalized Planning in PDDL Domains with Pretrained Large Language Models (opens in a new tab)
- ICLR (T1)Grounding Language Plans in Demonstrations through Counter-factual Perturbations (opens in a new tab)
- WAFRHybrid Declarative-Imperative Representations for Hybrid Discrete-Continuous Decision-Making (opens in a new tab)
- ICML (T1)Improving Factuality and Reasoning in Language Models through Multiagent Debate (opens in a new tab)
202330 papers
- CoRL (T1)Composable Part-Based Manipulation (opens in a new tab)
- ICLR (T1)Composing Ensembles of Pre-trained Models via Iterative Consensus (opens in a new tab)
- CoRL (T1)Compositional Diffusion-Based Continuous Constraint Solvers (opens in a new tab)
- CoLLAs-23 Workshop TrackContinual Improvement of Threshold-Based Novelty Detection (opens in a new tab)
- RSS (T1)Diffusion Policy: Visuomotor Policy Learning via Action Diffusion (opens in a new tab)
- CoRL (T1)Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation (opens in a new tab)
- CVPR WorkshopDistilled Feature Fields Enable Open-Ended Few-Shot Manipulation (opens in a new tab)
- CoLLAsEmbodied Active Learning of Relational State Abstractions for Bilevel Planning (opens in a new tab)
- CoRL (T1)Embodied Lifelong Learning for Task and Motion Planning (opens in a new tab)
- TMLR (T1)How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition (opens in a new tab)
- ICLR (T1)Is Conditional Generative Modeling all You Need for Decision-Making? (opens in a new tab)
- CoRL (T1)Learning Efficient Abstract Planning Models that Choose What to Predict (opens in a new tab)
Where alumni went122 listed
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
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