The verified answer
As of Oct 2026, DEPLOY verifies MIT CSAIL Locomotion Group: 1 model. 1 source back the record.
Overview
The MIT CSAIL Robot Locomotion Group, led by Professor Russ Tedrake, researches dynamics and control for humanoid robots, robotic manipulation, and dynamic walking over rough terrain. The group works on Atlas humanoid control, MPC, and learning-based approaches for legged locomotion.
Verified record
- Verified deployments
- None on file
- Active incidents
- None on file
Data & sources
Web sources
1
1 source backing this record.View all →
Models (1)
View all models →Partnerships
Every company MIT CSAIL Locomotion Group has built with, alongside the ones only announced in a press release.
Most-frequent partners
- 1Pegatron Corporation1× joint
Most recent
MIT CSAIL Locomotion Group on the deployment map
View the global mapMIT CSAIL Locomotion Group has no verified deployments on the DEPLOY registry yet. Explore the deployment map by place and type.
No verified deployments for MIT CSAIL Locomotion Group on the DEPLOY record yet. Track MIT CSAIL Locomotion Group to know the moment the first one is verified.
Relationships
Current leadership (7)
- Russ Tedrake Toyota Professor of EECS, Aero/Astro, MechE & Group Leadsecondary-verified
- Adam Wei PhD Candidate, EECSsecondary-verified
- Tommy Cohn PhD Candidate, EECSsecondary-verified
- Peter Werner PhD Candidate, EECSsecondary-verified
- Bernhard Paus Graesdal PhD Candidate, EECSsecondary-verified
- Taylan Kargin Postdoctoral Associatesecondary-verified
- Nicholas Pfaff PhD Candidate, EECSsecondary-verified
Former / Previously (5)
- Alexandre Amice PhD, EECS (2026)secondary-verified
- Tobia Marcucci PhD, EECSsecondary-verified
- Terry Suh PhD, EECS (2024)secondary-verified
- Lucas Manuelli PhD, EECS (2020)secondary-verified
- Boyuan Chen PhD, EECS (2025)secondary-verified
Safety record
No incidents on record for MIT CSAIL Locomotion Group.
Only active incidents are counted. Retracted incidents are excluded from this summary but remain reachable at their canonical URLs.
Full safety record: incidents, sourcing, and exposure data →
Recent coverage
MIT CSAIL Locomotion Group in third-party press
Peer companies
- Google21 models
- Roborock20 models
- ST Telemedia Global Data Centres11 models
- Unitree Robotics10 models
- Teraco Data Environments8 models
- AirTrunk7 models
Financial state
- Reporting basis
- aggregator_estimate
- Lifecycle stage
- early
- Counterparty risk class
- unknown
Each numeric field carries its own basis marker. Aggregators report a number; this surface preserves the source class so verification depth travels with the value.
Partnerships
1 total · 1 operating
Every company MIT CSAIL Locomotion Group has built with, alongside the ones only announced in a press release.
Most recent
Most-frequent partners
- 1Pegatron Corporation1× joint
All 1 partnerships
open in the atlas →- MIT CSAIL x Pegatron Robotics Research PartnershipwithPegatron Corporationdevelopment—operating
Recent activity
Every change to this record is dated, sourced, and independently verified where marked.
- Record createdAug 9, 2026
Added to the verified registry
Sources (1)
Common questions
What is MIT CSAIL Locomotion Group?
What does MIT CSAIL Locomotion Group make?
Is MIT CSAIL Locomotion Group publicly traded?
Who competes with MIT CSAIL Locomotion Group?
How can I invest in MIT CSAIL Locomotion Group?
Where is MIT CSAIL Locomotion Group headquartered?
Who owns MIT CSAIL Locomotion Group?
Where does MIT CSAIL Locomotion Group operate robots?
Is MIT CSAIL Locomotion Group a top robotics company?
Is MIT CSAIL Locomotion Group safe?
Methodology: Verified · 1 source (no primary) · last reviewed 2026-08-21
Verification posture
Verified
Low confidence
Review state
Stable
Last reviewed 2026-08-21
Sources by quality tier
- 1
- unclassified
- Unclassified source
The framework is documented at /methodology. Corrections at /corrections. Reviewer: DEPLOY editorial team.
Methodology surface for MIT CSAIL Locomotion Group.Peer companies
- Google21 models
- Roborock20 models
- ST Telemedia Global Data Centres11 models
- Unitree Robotics10 models
- Teraco Data Environments8 models
- AirTrunk7 models
In the press
Recent coverage mentioning MIT CSAIL Locomotion Group from third-party publications. Automatically surfaced; not part of the verified registry record.
AI agents create virtual playgrounds to help robots get crucial training data
SceneSmith uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms where robots can simulate everyday chores, providing…
Using generative AI to diversify virtual training grounds for robots
MIT CSAIL's Robot Locomotion Group created a tool that uses generative AI to create realistic virtual kitchens and living rooms where simulated robots can interact with models of…
Simulation-based pipeline tailors training data for dexterous robots
MIT's PhysicsGen system helps robots handle items in homes and factories by tailoring training data to a particular machine, turning VR demonstrations into thousands of…
New system enables robots to solve manipulation problems in seconds
MIT CSAIL researchers developed an algorithm that lets a robot 'think ahead' and consider thousands of potential motion plans simultaneously, enabling robots to solve manipulation…
Multimodal and reasoning LLMs supersize training data for dexterous robotic tasks
GenSim2 framework uses multimodal and reasoning large language models to supersize training data for robots, enabling dexterous manipulation task training at scale.
Boston Dynamics and Toyota Research Institute Announce Partnership to Advance Robotics Research
Boston Dynamics and TRI joined forces to combine TRI's Large Behavior Models with Boston Dynamics' Atlas robot. The partnership was co-led by Scott Kuindersma (Boston Dynamics)…
Creating and verifying stable AI-controlled systems in a rigorous and flexible way
MIT CSAIL researchers helped design a new technique that can guarantee the stability of robots controlled by neural networks, using Lyapunov functions to provide stability…
Robot Locomotion Group - MIT CSAIL
MIT CSAIL's Robot Locomotion Group researches dynamics and control for humanoid robots, robotic manipulation, and dynamic walking over rough terrain.
AI helps robots manipulate objects with their whole bodies
A new technique from MIT CSAIL enables a robot to reason efficiently about moving objects using more than just its fingertips, expanding manipulation capabilities to whole-body…
Robots can now pick up any object after inspecting it
CSAIL's Dense Object Nets (DON) system, developed by Pete Florence, Lucas Manuelli, and Russ Tedrake, enables robots to pick up novel objects after visual inspection, suggesting…
Programming drones to fly in the face of uncertainty
CSAIL's NanoMap system enables drones to avoid obstacles while flying at 20 miles per hour by deeply integrating sensing and control, developed by the Robot Locomotion Group.
Machine-readable surfaces
- Markdown mirror: /companies/mit-csail-locomotion.md
- RSS feed: /companies/mit-csail-locomotion/feed.xml
- JSON-LD: embedded in this page’s head
- REST API: /v1/companies/f954b8b4-81a6-47aa-abdb-300a69331289
- Revision history: /companies/mit-csail-locomotion/history
- Data documentation: /data
- Query this programmatically: Deploy MCP
Peer companies
- Google21 models
- Roborock20 models
- ST Telemedia Global Data Centres11 models
- Unitree Robotics10 models
- Teraco Data Environments8 models
- AirTrunk7 models
Heat Index
Serious52/100
A composite of competitive footprint, funding, press velocity, active partnerships, and supply-chain edges. Different from the Intelligence score below (which is per-form-factor productization). Non-robotics companies cap at 25.
Reality vs attention
MIT CSAIL Locomotion Group draws attention at the 26th percentile but verifies reality at the insufficient-data level among other robots.
6-month trend
Analysis
Limited public funding data for capital position assessment.
Dimension breakdown
Verified signal
Attention (reach, not merit)
DEPLOY Intelligence scores are computed from verified registry data: confirmed deployments, disclosed funding rounds, regulatory filings, active job listings, video viewership, and press coverage. Confidence ratings reflect data availability. Scores update nightly.
DEPLOY Indices — verified vs claimed
Last computed: Oct 6, 2026