A computer-implemented method comprising: receiving sensor data indicative of an environment at a current time; and determining, through inputting the sensor data into a trained machine learned model, whether the sensor data is indicative of a potential collision in the environment at a future time, the trained machine learned model being trained according to a process comprising: obtaining a log of sensor data indicative of an environment during a prior time period, wherein the log of sensor data comprises a sequence of image frames wherein the log of sensor data further comprises payload data indicative of a weight of a payload of a robotic device; determining that the log of sensor data relates to a physical collision between a physical object in the environment and the robotic device at a particular time within the prior time period, wherein determining that the log of sensor data relates to the collision comprises determining that the payload data is indicative of an unexpected decrease in the weight of the payload; responsive to determining that the log of sensor data indicates the occurrence of the collision, generating a training data set for the collision from the log of sensor data, wherein the training data set for the collision comprises multiple image frames of the sequence of image frames that are prior to an image frame in the sequence of image frames that corresponds to the particular time; and training the machine learned model with the training data set to predict future collisions. receiving sensor data indicative of an environment at a current time; and determining, through inputting the sensor data into a trained machine learned model, whether the sensor data is indicative of a potential collision in the environment at a future time, the trained machine learned model being trained according to a process comprising: obtaining a log of sensor data indicative of an environment during a prior time period, wherein the log of sensor data comprises a sequence of image frames wherein the log of sensor data further comprises payload data indicative of a weight of a payload of a robotic device; determining that the log of sensor data relates to a physical collision between a physical object in the environment and the robotic device at a particular time within the prior time period, wherein determining that the log of sensor data relates to the collision comprises determining that the payload data is indicative of an unexpected decrease in the weight of the payload; responsive to determining that the log of sensor data indicates the occurrence of the collision, generating a training data set for the collision from the log of sensor data, wherein the training data set for the collision comprises multiple image frames of the sequence of image frames that are prior to an image frame in the sequence of image frames that corresponds to the particular time; and training the machine learned model with the training data set to predict future collisions. obtaining a log of sensor data indicative of an environment during a prior time period, wherein the log of sensor data comprises a sequence of image frames wherein the log of sensor data further comprises payload data indicative of a weight of a payload of a robotic device; determining that the log of sensor data relates to a physical collision between a physical object in the environment and the robotic device at a particular time within the prior time period, wherein determining that the log of sensor data relates to the collision comprises determining that the payload data is indicative of an unexpected decrease in the weight of the payload; responsive to determining that the log of sensor data indicates the occurrence of the collision, generating a training data set for the collision from the log of sensor data, wherein the training data set for the collision comprises multiple image frames of the sequence of image frames that are prior to an image frame in the sequence of image frames that corresponds to the particular time; and training the machine learned model with the training data set to predict future collisions.