This tracker collects Nvidia and Intel patent filings on teaching robots to grasp objects, navigate around obstacles, correct sensor drift, and learn from their own failures. Together the filings show two chipmakers racing to turn lab demonstrations into robots that behave reliably outside the lab.
36 filings · tracking since Apr 2026 · latest Jul 2026 · updates automatically as new filings publish
US 2026/0195585 A1
Generating physics-accurate video from text descriptions lets robots learn collision avoidance and object dynamics in simulation before touching real warehouses, compressing months of trial-and-error into synthetic training data.
US 2026/0192452 A1
Within the gripper-control problem, this filing shows how to run reachability and collision-checking together rather than as separate steps, cutting the decision time a robot arm needs before it moves.
US 2026/0196027 A1
A world foundation model generates predicted video of the target task, letting robots preview actions before execution, confirming the tracker's focus on learning from visual simulation rather than trial-and-error in the physical world.
US 2026/0196032 A1
Synthetic video generation skips the physical trial-and-error loop by letting an AI create realistic robot demonstrations from workspace images and task descriptions, then uses those videos as training data.
US 2026/0195951 A1
Where the storyline has focused on learning from real failures, this filing shows robots could instead practice in physics-accurate simulations, collapsing trial-and-error into virtual space before touching actual objects.
US 2026/0192447 A1
Sensor gloves paired with headset tracking split the workload between hand-detail capture and full-body positioning, letting a human operator demonstrate complex manipulation tasks to a robot in real time rather than programming each movement.
US 2026/0187836 A1
Within the grasp-and-move storyline, this filing shows how robots can learn placement tasks from minimal human input, moving past the hundreds-of-example bottleneck by extracting spatial logic from a single demonstration video.
US 2026/0186457 A1
A correction layer that monitors prediction errors and updates the physics model between movements, keeping the robot's control calculations aligned with real-world conditions as they drift.
US 2026/0187990 A1
Previous grasp failures often stem from misreading object geometry or category. This filing merges shape and identity recognition into one step, reducing the mismatch errors that cause awkward approaches.
US 2026/0182802 A1
A robot that removes obstacles instead of stopping at them cuts down the trial-and-error cycles needed to navigate real homes. The mechanical gripper solves the navigation problem by making clutter moveable rather than trying to route around it.
US 2026/0183950 A1
Translating natural language commands into robot motion by learning from paired video and voice narration sidesteps the need to manually code movement parameters for each task.
US 2026/0175427 A1
Robots executing fixed plans crash when sensor data degrades mid-task. Intel's filing shows how to reweight a robot's priorities on the fly, trading off task completion speed against uncertainty about what the robot can actually sense in real time.
US 2026/0166747 A1
Decomposing vague commands into executable sequences, finding the gap between "move the cup" and the dozen micro-actions required to actually do it.
US 2026/0169478 A1
A depth camera lets the robot scan multiple height layers at once and select whichever one contains the clearest landmarks for navigation, solving the problem of getting stuck when a single camera angle sees only clutter.
US 2026/0166758 A1
A robot that spreads its fingers before grasping could handle objects ranging from thin cards to wide bottles without reprogramming between tasks, reducing the need for multiple specialized grippers.
US 2026/0169502 A1
The patents so far show robots learning to manipulate physical objects. Samsung's filing extends the problem into the digital area: a robot must read a screen, understand what it shows, and operate touchscreens or remote interfaces as a human would.
US 2026/0165599 A1
Powered joints that enforce correct motion sequences during exercise represent a shift from passive support to active movement correction, addressing how robots learn limb positioning through physical guidance rather than visual observation alone.
US 2026/0166725 A1
A robot that corrects its own mistakes without human intervention moves the grasp problem from initial training to real-world durability, where small execution errors compound into mission failures.
US 2026/0166732 A1
The storyline so far has focused on object manipulation; Samsung shifts the lens to planning failures when a robot's mental map proves incomplete, forcing real-time replanning rather than task abandonment.
US 2026/0158650 A1
When a robot arm gets physically stuck mid-task, it normally needs human rescue. Samsung's filing shows how onboard AI can detect these deadlock states and compute escape paths without outside intervention.
US 2026/0161179 A1
Earlier filings focused on grasping and object manipulation; this one zooms out to the locomotion layer itself, solving how wheeled robots stay on curved paths without drift by individually controlling wheel speeds based on predictive trajectory data.
US 2026/0153883 A1
A robot that stops smoothly instead of lurching to a halt could reliably set down objects without spilling or dropping them, a basic requirement for any real warehouse work.
US 2026/0151914 A1
The storyline so far treats vision as fixed; Samsung's approach makes it mobile, letting robots reposition their own cameras to see past obstacles rather than work around blind spots.
US 2026/0154969 A1
Verification loops stand out as the missing piece: Samsung's patent adds machine vision that compares floor conditions before and after cleaning, letting robots detect when stains persist and decide whether to retry a spot.
US 2026/0154645 A1
A central monitoring system tracks worker movement in real time and feeds dynamic route adjustments directly to the robot fleet, replacing the pre-planned paths that create bottlenecks when humans cluster in one area.
US 2026/0151916 A1
A robot that reads human gaze could pick objects from cluttered spaces without needing explicit verbal commands, reducing the real-time communication overhead that slows down human-robot collaboration in actual homes and warehouses.
US 2026/0145333 A1
Simulating touch feedback without perfect object positions pushes training into territory where robots must learn from incomplete sensory data, mirroring real-world grasping constraints.
US 2026/0145332 A1
A two-stage simulation approach separates learning object geometry from learning real-world motor control, letting the robot master spatial reasoning before encountering sensory noise and delays.
US 2026/0145322 A1
The robot grasping problem requires training data that doesn't overwhelm the learning system from the start. Nvidia's approach sequences tasks by difficulty, letting the AI build competence incrementally rather than failing on hard cases too early.
US 2026/0138272 A1
Robots could infer object weight from human posture during handoffs, eliminating the need to sense weight directly at the moment of grasping and reducing grip failures from misjudged force.
US 2026/0138268 A1
Automated detection of human interventions during robot operations creates a continuous feedback loop for retraining without manual annotation, converting real-world failures into training data at scale.
US 2026/0131474 A1
The storyline so far has focused on training robots to physically handle objects. This filing pivots to the perceptual problem: giving robots spatial memory so they can navigate without constant repositioning.
US 2026/0131467 A1
Sensor drift compounds during long warehouse runs, forcing robots to periodically recalibrate. Intel's dual-sensor fusion weights each signal's reliability to maintain continuous position accuracy without stopping.
US 2026/0115906 A1
Separating grasp point prediction from grip planning into two distinct neural networks lets each stage specialize rather than forcing one model to solve both problems simultaneously.
US 2026/0115905 A1
Diffusion models, normally used for image generation, retrain here as simulators that generate candidate grasp sequences from scratch, letting the system learn which approaches work before robots try them physically.
US 2026/0115904 A1
A robot trained in simulation can now adjust its own behavior when real-world physics don't match predictions, eliminating the costly trial-and-error phase that usually follows deployment.
Most of the filings in this storyline focus on getting a robot hand to grasp objects that were never part of its training set. Nvidia alone has filed a two-stage grasping pipeline, a diffusion-based grasping system, a teacher-student loop for dexterous grasping, a progressive difficulty training loop, and a geometric fabrics approach to smoother motion. A separate Nvidia filing addresses a self-correcting sim-to-real loop, and another describes a system that lets a robot learn from its own failed attempts instead of discarding that data. The repetition itself is the signal: grasping remains unsolved enough that one company keeps filing new approaches.
Intel's filings sit next to Nvidia's but attack different pieces of the same puzzle. One patent has a robot predict what's around a corner it can't see, another fuses sensors to stop a robot from drifting off its intended path, and a third estimates an object's weight from a person's posture before handoff. Samsung's two entries are narrower and mechanical: a two-stage braking system for wheeled robots and a robot that repositions its own sensor to see past obstacles. Together these show the same practical concerns as Nvidia's grasping work, applied to movement and perception instead.
Read across the batch, most filings target the same handful of problems: reliable grasping, staying on course, sensing around obstacles, and using failed attempts as training data. Readers should watch whether new filings keep refining these existing approaches from Nvidia and Intel, or whether other manufacturers beyond Samsung start filing in the same space with genuinely different techniques. The presence of Samsung's mechanical patents alongside Nvidia and Intel's AI-heavy filings suggests the race is not confined to software alone.
They're mostly working on the same core problem: getting robots to reliably grasp objects and move through real environments without constant human correction. The filings show sim-to-real training loops, sensor fusion for drift, and systems that let robots learn from failed attempts. These are research filings, not confirmed products, so they show direction rather than finished technology.
No. A patent filing describes an idea a company wants to protect, not a shipping product. Nvidia and Intel file broadly across grasping, navigation, and perception, and only some of these ideas will end up in real robots. The filings are a useful signal of where engineering attention is going, not a roadmap.
Grasping keeps showing up because picking up an unfamiliar object reliably is still unsolved in robotics. Nvidia has filed multiple grasping approaches, including diffusion-based and teacher-student methods, which suggests the company sees this as worth attacking from several angles at once rather than settling on one method.
Samsung's filings are more mechanical than Nvidia's or Intel's, covering a two-stage braking system for rolling robots and a robot that repositions its own sensor to see around obstacles. That shows the same reliability concerns, staying in control and seeing clearly, showing up in hardware design and not just AI training methods.
Which company should we read for you?
We track 17 companies here. Pro is the same weekly breakdown for any company you choose, delivered privately. Type a name and we'll scope it and send you a quote.